1,401 Free CS Courses from MIT, Stanford and IITs to Learn by 2027

Published September 2026 · avivashishta.com

You commented cs, so here are all 1,401 courses, free, with a link for each. These aren't random YouTube videos. They're full university courses with lecture recordings, from MIT, Stanford, Harvard, CMU, Berkeley, the IITs and more, collected in one GitHub repo. I checked every GitHub-hosted link on 29 September 2026 and dropped the dead ones. The rest come from a list its maintainers keep updated, so if a course has moved, tell me and I'll fix it.

The short version: 1,401 courses across 25 subjects, from DSA to quantum computing. 410 of them are machine learning and AI, including 37 on generative AI and LLMs. If you want an AI engineer job, do the 4 below, build one project after each, and use the rest as a reference.

The repo

Developer-Y/cs-video-courses ~83K ★

github.com/Developer-Y/cs-video-courses

A list of college-level computer science courses that come with video lectures. MIT licensed, free forever, and organised by subject.

The 4 I'd start with (and what to build after each)

  1. Harvard CS50 if you're new to coding. Build: your first web app, deployed with a live link.
  2. MIT 6.S191: Introduction to Deep Learning. Build: an image classifier trained on your own photos.
  3. Stanford CS224N: NLP with Deep Learning. Build: a sentiment analyser for Instagram comments.
  4. Stanford CS336: Language Modeling from Scratch. Build: a tiny language model trained on your WhatsApp chats.

You don't need all 1,401 courses. You need a few of them, finished, with a project for each on your GitHub.

Subjects

Introduction to Computer Science (36) · Data Structures and Algorithms (101) · Systems Programming (7) · Operating Systems (23) · Distributed Systems (21) · Real-Time Systems (7) · Database Systems (22) · Object Oriented Design (10) · Software Engineering (15) · Software Architecture (3) · Concurrency (11) · Mobile Application Development (13) · Artificial Intelligence (27) · Introduction to Machine Learning (124) · Data Mining (18) · Probabilistic Graphical Modeling (4) · Deep Learning (59) · Reinforcement Learning (35) · Advanced Machine Learning (11) · Natural Language Processing (26) · Generative AI and LLMs (37) · Computer Vision (18) · Time Series Analysis (2) · Optimization (28) · Unsupervised Learning (8) · Misc Machine Learning Topics (40) · Computer Networks (30) · Math for Computer Scientist (129) · Web Programming and Internet Technologies (16) · Theoretical CS and Programming Languages (68) · Embedded Systems (25) · Real time system evaluation (14) · Computer Organization and Architecture (51) · Security (37) · Computer Graphics (22) · Image Processing and Computer Vision (38) · Computational Physics (16) · Computational Biology (38) · Quantum Computing (29) · Robotics and Control (92) · Computational Finance (18) · Network Science (5) · Blockchain Development (12) · Misc (55)

The courses, by subject (1401)

Source: github.com/Developer-Y/cs-video-courses

Introduction to Computer Science (36)

  1. COMP6991 - Rust Programming - UNSW (2022)
  2. COMP1511 - Programming Fundamentals - UNSW (Complete Course: Lectures, Labs, Assignments, Exercises, Exams): Video Playlist
  3. CS 10 - The Beauty and Joy of Computing - Spring 2015 - Dan Garcia - UC Berkeley InfoCoBuild
  4. 6.0001 - Introduction to Computer Science and Programming in Python - MIT OCW
  5. 6.001 - Structure and Interpretation of Computer Programs, MIT
  6. Introduction to Computational Thinking - MIT
  7. CS 50 - Introduction to Computer Science, Harvard University: (cs50.tv)
  8. CS50R - Introduction to Programming with R: (Lecture Videos)
  9. CS50P - CS50's Introduction to Programming with Python - Harvard (David J. Malan): (Lecture Videos)
  10. CS50's Understanding Technology
  11. CSE 142 Computer Programming I (Java Programming), Spring 2016 - University of Washington
  12. CS 1301 Intro to computing - Gatech
  13. CS 106A - Programming Methodology, Stanford University: (Lecture Videos)
  14. CS 106B - Programming Abstractions, Stanford University
  15. CS 106L - Standard C++ Programming: (Lecture Videos)
  16. CS 106X - Programming Abstractions in C++
  17. CS 107 - Programming Paradigms, Stanford University
  18. CmSc 150 - Introduction to Programming with Arcade Games, Simpson College
  19. IN2377 - Concepts of C++ programming (Winter 2023), TUM: (Winter 2022) (Summer 2022) (Summer 2021)
  20. IN1503 - Advanced C++ Programming, TUM
  21. LINFO 1104 - Paradigms of computer programming, Peter Van Roy, Université catholique de Louvain, Belgium - EdX
  22. FP 101x - Introduction to Functional Programming, TU Delft
  23. Introduction to Problem Solving and Programming - IIT Kanpur
  24. Introduction to programming in C - IIT Kanpur
  25. Programming in C++ - IIT Kharagpur
  26. Python Boot Camp Fall 2016 - Berkeley Institute for Data Science (BIDS)
  27. CS 101 - Introduction to Computer Science - Udacity
  28. 6.00SC - Introduction to Computer Science and Programming (Spring 2011) - MIT OCW
  29. 6.00 - Introduction to Computer Science and Programming (Fall 2008) - MIT OCW
  30. 6.01SC - Introduction to Electrical Engineering and Computer Science I - MIT OCW
  31. Modern C++ Course (2018) - Bonn University
  32. Modern C++ (Lecture & Tutorials, 2020, Vizzo & Stachniss) - University of Bonn
  33. UW Madison CS 368 C++ for Java Programmers Fall 2020, by Michael Doescher
  34. UW Madison CS 354 Machine Organization and Programming spring 2020, 2021, by Michael Doescher
  35. Cornell CS 1110 Introduction to Computing using Python fall 2020, by Walker White: (Lecture Videos)
  36. Cornell ECE 4960 Computational and Software Engineering spring 2017, by Edwin Kan

Data Structures and Algorithms (101)

  1. ECS 36C - Data Structures and Algorithms (C++) - Spring 2020 - Joël Porquet-Lupine - UC Davis
  2. Programming and Data Structures with Python, 2021-2022, Sem I - by Prof. Madhavan Mukund, CMI
  3. Graph Algorithms - Robert Sedgewick - Princeton University
  4. EECS 477 - Introduction to Algorithms, Winter 2023, UMichigan
  5. EECS 498 / 598 - Advanced Graph Algorithms: Graph Algorithms via Graph Decomposition, Fall 2025, UMichigan
  6. EECS 498 / 598 - Advanced Graph Algorithms: Expanders and Fast Graph Algorithms, Fall 2021, UMichigan
  7. 6.006 - Introduction to Algorithms, MIT OCW
  8. MIT 6.006 Introduction to Algorithms, Spring 2020
  9. Algorithms: Design and Analysis 1 - Stanford University
  10. Algorithms: Design and Analysis 2 - Stanford University
  11. COS 226 Algorithms, Youtube, Princeton - by Robert Sedgewick and Kevin Wayne
  12. CSE 331 Introduction to Algorithm Design and Analysis, SUNY University at Buffalo, NY - Fall 2017: (Lectures) (Homework Walkthroughs)
  13. CSE 373 - Analysis of Algorithms, Stony Brook - Prof Skiena
  14. COP 3530 Data Structures and Algorithms, Prof Sahni, UFL: (Videos)
  15. CS225 - Data Structures - University of Illinois at Urbana-Champaign: (Video lectures)
  16. CS2 - Data Structures and Algorithms Sim-Mautner - UNSW
  17. Data Structures - Pepperdine University
  18. CS 161 - Design and Analysis of Algorithms, Prof. Tim Roughgarden, Stanford University
  19. 6.046J - Introduction to Algorithms - Fall 2005, MIT OCW
  20. Introduction to Algorithms (Spring 2020), MIT OCW
  21. 6.046 - Design and Analysis of Algorithms, Spring 2015 - MIT OCW
  22. CS 473 - Algorithms - University of Illinois at Urbana-Champaign: (Notes - Jeff Erickson) (YouTube)
  23. COMP300E - Programming Challenges, Prof Skiena, Hong Kong University of Science and Technology - 2009
  24. 16s-4102 - Algorithms, University of Virginia: (Youtube)
  25. CS 61B - Data Structures (Java) - UC Berkeley: (Discussion 2022)
  26. CS 170 Algorithms - UCBerkeley: Fall 2019, Youtube Fall 2018, Youtube Fall 2018,Bilibili 2013 Bilibili
  27. CS 159 Data-Driven Algorithm Design - Caltech: Spring 2020, Youtube
  28. ECS 122A - Algorithm Design and Analysis, UC Davis
  29. CSE 373 - Data Structures and Algorithms, Winter 2024 - University of Washington: (Winter 2024, Youtube) (Spring 2023, Notes) (Spring 2023, Youtube)
  30. CSEP 521 - Applied Algorithms, Winter 2013 - University of Washington: (Videos)
  31. Data Structures And Algorithms - IIT Delhi
  32. Design and Analysis of Algorithms - IIT Bombay
  33. Programming, Data Structures and Algorithms - IIT Madras
  34. Design and Analysis of Algorithms - IIT Madras
  35. Fundamental Algorithms:Design and Analysis - IIT Kharagpur
  36. Programming and Data Structure - IIT Kharagpur
  37. Programming, Data structures and Algorithms - IIT Madras
  38. Programming, Data Structures and Algorithms in Python - IIT Madras
  39. Programming and Data structures (PDS) - IIT Madras
  40. COP 5536 Advanced Data Structures, Prof Sahni - UFL: (Videos)
  41. CS 261 - A Second Course in Algorithms, Stanford University: (Youtube)
  42. CS 224 - Advanced Algorithms, Harvard University: (Lecture Videos) (Youtube)
  43. CS 6150 - Advanced Algorithms (Fall 2016), University of Utah
  44. CS 6150 - Advanced Algorithms (Fall 2017), University of Utah
  45. ECS 222A - Graduate Level Algorithm Design and Analysis, UC Davis
  46. 6.851 - Advanced Data Structures, MIT: (MIT OCW)
  47. 6.854 - Advanced Algorithms, MIT: (Prof. Karger lectures)
  48. CS264 Beyond Worst-Case Analysis, Fall 2014 - Tim Roughgarden Lecture: (Youtube)
  49. CS364A Algorithmic Game Theory, Fall 2013 - Tim Roughgarden Lectures
  50. CS364B Advanced Mechanism Design, Winter 2014 - Tim Roughgarden Lectures
  51. CS3510 Algorithms, Spring 2024 - Georgia Tech: Videos - Sp 2024 Videos - Su 2025
  52. CS4510 Automata and Complexity - Georgia Tech: Videos - Su 2021 Videos - Su 2023 Videos - Su 2024 Videos - Su 2025
  53. Algorithms - Aduni
  54. 6.889 - Algorithms for Planar Graphs and Beyond (Fall 2011) MIT
  55. 6.890 Algorithmic Lower Bounds: Fun with Hardness Proofs - MIT OCW
  56. Computer Algorithms - 2 - IIT Kanpur
  57. Parallel Algorithm - IIT Kanpur
  58. Graph Theory - IISC Bangalore
  59. Data Structures - mycodeschool
  60. Algorithmic Game Theory, Winter 2020/21 - Uni Bonn
  61. NETS 4120: Algorithmic Game Theory, Spring 2023 - UPenn
  62. Introduction to Game Theory and Mechanism Design - IIT Kanpur
  63. 15-850 Advanced Algorithms - CMU Spring 2023
  64. CS 270. Combinatorial Algorithms and Data Structures, Spring 2021: (Youtube)
  65. UC Berkeley CS 294-165 Sketching Algorithms fall 2020, by Jelani Nelson: (Youtube)
  66. UIUC CS 498 ABD / CS 598 CSC Algorithms for Big Data fall 2020, by Chandra Chekuri
  67. Algorithms for Data Science spring 2021, by Anil Maheshwari
  68. CMU 15 859 Algorithms for Big Data fall 2020, by David Woodruff
  69. CO 642 Graph Theory - University of Waterloo
  70. COMS W4241 Numerical Algorithms spring 2006, by Henryk Wozniakowski - Columbia
  71. Bonn Algorithms and Uncertainty summer 2021, by Thomas Kesselheim
  72. Harvard Information Theory 2022, by Gregory Falkovich
  73. Math 510 - Linear Programming and Network Flows - Colorado State University
  74. LINFO 2266 Advanced Algorithms for Optimization 2021, by Pierre Schaus - UCLouvain
  75. MIT 6.5210 / 6.854 / 18.415 Advanced Algorithms Fall 2013, 2020, 2021, 2022, by David Karger: (Spring 2016, by Ankur Moitra)
  76. CMU 10 801 Advanced Optimization and Randomized Algorithms spring 2014, by Suvrit Sra and Alex Smola
  77. Purdue CS 381 Fundamental Algorithms, by Kent Quanrud: (Spring 2022)
  78. Purdue CS 390 ATA Fundamental Algorithms Advanced, by Kent Quanrud: (Spring 2025)
  79. Purdue CS 580 Graduate Algorithms, by Kent Quanrud: (Spring 2023) (Spring 2024)
  80. Purdue CS 588 Randomized Algorithms, by Kent Quanrud: (Fall 2022) (Spring 2024)
  81. UC Santa Cruz CSE 101 Intro to Data Structures and Algorithms fall 2022, by Seshadhri Comandur: (Fall 2020)
  82. UC Santa Cruz CSE 201 Analysis of Algorithms winter 2022, by Seshadhri Comandur
  83. UC Santa Cruz CSE 202 Combinatorial Algorithms spring 2021, by Seshadhri Comandur
  84. UC Santa Cruz CSE 104, 204 Computational Complexity spring 2022, by Seshadhri Comandur: (Fall 2020)
  85. UC Santa Cruz CSE 290A Randomized Algorithms spring 2020, by Seshadhri Comandur
  86. University of Maryland CMSC351 Introduction to Algorithms, by Mohammad Hajiaghayi
  87. University of Maryland CMSC858F Network Algorithms and Approximations, by Mohammad Hajiaghayi: (YouTube playlists)
  88. University of Maryland CMSC858M Algorithmic Lower Bounds: Fun with Hardness Proofs, by Mohammad Hajiaghayi: (YouTube playlists)
  89. University of Maryland UMD DATA602 / MSML602 Principles of Data Science spring 2024, by Mohammad Hajiaghayi
  90. Algorithms for Big-Data (Fall 2020) - Saket Saurabh
  91. CS498ABD - Algorithms for Big Data - UIUC, Fall 2020
  92. Advanced Data Structures
  93. CS60025 Algorithmic Game Theory - IIT KGP - Winter 2020
  94. CS60083 Parameterized Algorithms - IIT KGP
  95. Parameterized Complexity
  96. Structural Graph Theory - IIT Madras
  97. Information Theory - IISC Bangalore
  98. 6.5220/6.856J/18.416J Randomized Algorithms (Spring 2025): (Youtube)
  99. 18.225 - Graph Theory and Additive Combinatorics - MIT - Fall 2023
  100. Extremal graph theory at KAIST
  101. Advanced graph theory, KAIST 2020

Systems Programming (7)

  1. COMP1521 - Computer Systems Fundamentals (2025) - UNSW (Full Course: Website + Lectures, Labs, Assignments, Exercises, Exams, Video Playlist): Watch Lectures
  2. Computer Systems: A programmer's Perspective
  3. CS361 - COMPUTER SYSTEMS - UIC
  4. CS 3650 - Computer Systems - Fall 2020 - Nat Tuck - NEU: (Lectures - YouTube)
  5. CS 4400 – Computer Systems Fall 2016 - UoUtah
  6. Systems - Aduni
  7. CS110: Principles of Computer Systems - Stanford

Operating Systems (23)

  1. Gatech CS6200 Intro to OS
  2. APS 105 - Computer Fundamentals - Winter 2025 - Jon Eyolfson - University of Toronto: (Winter 2024)
  3. ECS 150 - Operating Systems and Systems Programming - Fall 2020 - Joël Porquet-Lupine - UC Davis
  4. ECE 344 - Operating Systems - Fall 2024 - Jon Eyolfson - University of Toronto: (Fall 2024 Section 2) (Fall 2023) (Fall 2022)
  5. ECE 353 - Systems Software - Winter 2025 - Jon Eyolfson - University of Toronto: (Winter 2024) (Winter 2023)
  6. ECE 454 - Computer Systems Programming - Fall 2024 - Jon Eyolfson - University of Toronto
  7. CS124 Operating Systems - California Institute of Technology, Fall 2018 - Youtube
  8. CS 162 Operating Systems and Systems Programming, Spring 2015 - University of California, Berkeley: (Fall 2020 - YouTube)
  9. CS 4414 - Operating Systems, University of Virginia (rust-class)
  10. CS 4414 Operating Systems, Fall 2018 - University of Virginia
  11. CSE 421/521 - Introduction to Operating Systems, SUNY University at Buffalo, NY - Spring 2016: (Lectures - YouTube) (Recitations 2016) (Assignment walkthroughs)
  12. CS 377 - Operating Systems, Fall 16 - Umass OS
  13. CS 577 - Operating Systems, Spring 20 - Umass OS
  14. CS 537 - Introduction to Operating Systems - Andrea & Remzi Arpaci-Dusseau, University of Wisconsin-Madison: (OSTEP free textbook) (Lecture Videos)
  15. CSE 30341 - Operating Systems, Spr 2008
  16. CSEP 551 Operating Systems Autumn 2014 - University of Washington
  17. Introduction to Operating Systems - IIT Madras
  18. CS194 Advanced Operating Systems Structures and Implementation, Spring 2013 InfoCoBuild, UC Berkeley
  19. CSE 60641 - Graduate Operating Systems, Fall 08
  20. Advanced Programming in the UNIX Environment
  21. Operating System - IIT Madras
  22. CS 153 Frontier Systems - Stanford University - Spring 2026
  23. EE415: Introduction to Operating Systems (KAIST)

Distributed Systems (21)

  1. CS 677 - Distributed Operating Systems, Spring 24 - Umass OS
  2. CS 436 - Distributed Computer Systems - U Waterloo
  3. 6.824 - Distributed Systems, Spring 2015 - MIT
  4. 6.824 Distributed Systems - Spring 2020 - MIT: (Youtube)
  5. Distributed Systems Lecture Series
  6. Distributed Algorithms, https://canvas.instructure.com/courses/902299
  7. CSEP 552 - PMP Distributed Systems, Spring 2013 - University of Washington: (Videos)
  8. CSE 490H - Scalable Systems: Design, Implementation and Use of Large Scale Clusters, Autumn 2008 - University of Washington: (Videos)
  9. MOOC - Cloud Computing Concepts - UIUC
  10. Distributed Systems (Prof. Pallab Dasgupta)
  11. EdX KTHx ID2203 Reliable Distributed Algorithms
  12. Distributed Data Management - Technische Universität Braunschweig, Germany
  13. Information Retrieval and Web Search Engines - Technische Universität Braunschweig, Germany
  14. Middleware and Distributed Systems (WS 2009/10) - Dr. Martin von Löwis - HPI
  15. CSE 138 - Distributed Systems - UC Santa Cruz, Spring 2020: (2021)
  16. CMU 15 440 / 640 Distributed Systems Spring 2022, by Mahadev Satyanarayanan, Padmanabhan Pillai
  17. UNC Comp533 - Distributed Systems Spring 2020
  18. Brown CSCI 1380 Distributed Computer Systems spring 2016, by Tom Doeppner & Rodrigo Fonseca
  19. Distributed Algorithms - Jukka Suomela
  20. Programming Parallel Computers - Jukka Suomela
  21. Gatech CS7210 Distributed Computing

Real-Time Systems (7)

  1. CPCS 663 - Real-Time Systems: Video Material - TAMU
  2. Real Time Systems - IIT Kharagpur
  3. 6.172 Performance Engineering of Software Systems - MIT OCW
  4. Performance Evaluation of Computer Systems - IIT Madras
  5. Storage Systems - IISC Bangalore
  6. MAP6264 - Queueing Theory - FAU: (Video Lectures)
  7. EE 380 Colloquium on Computer Systems - Stanford University: (Lecture videos)

Database Systems (22)

  1. CMPSC 431W Database Management Systems, Fall 2015 - Penn State University: Lectures - YouTube
  2. CS121 - Introduction to Relational Database Systems, Fall 2016 - Caltech
  3. CS 5530 - Database Systems, Spring 2016 - University of Utah
  4. Distributed Data Management (WT 2018/19) - HPI University of Potsdam
  5. MOOC - Database Stanford Dbclass
  6. CSEP 544, Database Management Systems, Au 2015 - University of Washington
  7. Database Design - IIT Madras
  8. Fundamentals of Database Systems - IIT Kanpur
  9. Principles of Database Management, Bart Baesens
  10. FIT9003 Database Systems Design - Monash University
  11. 15-445 - Introduction to Database Systems, CMU: (YouTube-2017), (YouTube-2018),(YouTube-2019), (YouTube-2021), (YouTube-2022),(YouTube-2023),(YouTube-2024),(YouTube-2025)
  12. 15-721 - Advanced Database Systems, CMU: (YouTube-2024, YouTube-2023, YouTube-2022)
  13. CS122 - Relational Database System Implementation, Winter 2014-2015 - Caltech
  14. CS 186 - Database Systems, UC Berkeley, Spring 2015
  15. CS 6530 - Graduate-level Database Systems, Fall 2016, University of Utah: (Lectures - YouTube)
  16. 6.830/6.814 - Database Systems Fall 2014
  17. Informatics 1 - Data & Analysis 2014/15- University of Edinburgh
  18. Database Management Systems, Aduni
  19. D4M - Signal Processing on Databases
  20. In-Memory Data Management (2013)Prof. Hasso Plattner - HPI
  21. Distributed Data Management (WT 2019/20) - Dr. Thorsten Papenbrock - HPI
  22. CS122d - NoSQL Data Management (Spring 21) - Prof. Mike Carey - UC Irvine

Object Oriented Design (10)

  1. ECE 462 Object-Oriented Programming using C++ and Java - Purdue
  2. Object-oriented Program Design and Software Engineering - Aduni
  3. OOSE - Object-Oriented Software Engineering, Dr. Tim Lethbridge
  4. Object Oriented Systems Analysis and Design (Systems Analysis and Design in a Changing World)
  5. CS 251 - Intermediate Software Design (C++ version) - Vanderbilt University
  6. OOSE - Software Dev Using UML and Java
  7. Object-Oriented Analysis and Design - IIT Kharagpur
  8. CS3 - Design in Computing - Richard Buckland UNSW
  9. Informatics 1 - Object-Oriented Programming 2014/15- University of Edinburgh
  10. Software Engineering with Objects and Components 2015/16- University of Edinburgh

Software Engineering (15)

  1. COMP1531 - Software Engineering Fundamentals - UNSW (2025): Video Playlist
  2. Computer Science 169- Software Engineering - Spring 2015 - UCBerkeley
  3. Computer Science 169- Software Engineering - Fall 2019 - UCBerkeley
  4. CS 5150 - Software Engineering, Fall 2014 - Cornell University
  5. Introduction to Service Design and Engineering - University of Trento, Italy
  6. CS 164 Software Engineering - Harvard
  7. System Analysis and Design - IISC Bangalore
  8. Software Engineering - IIT Bombay
  9. Dependable Systems (SS 2014)- HPI University of Potsdam
  10. Automated Software Testing - ETH Zürich | Spring 2024
  11. Software Testing - IIT Kharagpur
  12. Software Testing - Udacity, course-cs258 | 2015
  13. Software Debugging - Udacity, course-cs259 | 2015
  14. Software Engineering - Bauhaus-Uni Weimar
  15. CMU 17-445 Software Engineering for AI-Enabled Systems summer 2020, by Christian Kaestner

Software Architecture (3)

  1. CS 411 - Software Architecture Design - Bilkent University
  2. MOOC - Software Architecture & Design - Udacity
  3. CS-310 Scalable Software Architectures

Concurrency (11)

  1. CS176 - Multiprocessor Synchronization - Brown University: (Videos from 2012)
  2. CS 282 (2014): Concurrent Java Network Programming in Android
  3. CSE P 506 – Concurrency, Spring 2011 - University of Washington: (Videos)
  4. CSEP 524 - Parallel Computation - University of Washington: (Videos)
  5. Parallel Programming Concepts (WT 2013/14) - HPI University of Potsdam
  6. Parallel Programming Concepts (WT 2012/13) - HPI University of Potsdam
  7. UIUC ECE 408 / CS 408 Applied Parallel Programming fall 2022, by Wen-mei Hwu, Sanjay Patel: (Spring 2018)
  8. UIUC ECE 508 / CS 508 Manycore Parallel Algorithms spring 2019, by Wen-mei Hwu
  9. UIUC CS 420 / ECE 492 / CSE 402 Introduction to Parallel Programming for Scientists and Engineers fall 2015, by Sanjay Kale
  10. Stanford CME 213 Introduction to Parallel Computing using MPI, openMP, and CUDA winter 2020, by Eric Darve
  11. KAIST CS431: Concurrent Programming

Mobile Application Development (13)

  1. MOOC Programming Mobile Applications for Android Handheld Systems - University of Maryland
  2. CS 193p - Developing Applications for iOS, Stanford University
  3. CS S-76 Building Mobile Applications - Harvard
  4. CS 251 (2015): Intermediate Software Design
  5. Android App Development for Beginners Playlist - thenewboston
  6. Android Application Development Tutorials - thenewboston
  7. MOOC - Developing Android Apps - Udacity
  8. MOOC - Advanced Android App Development - Udacity
  9. CSSE490 Android Development Rose-Hulman Winter 2010-2011, Dave Fisher
  10. iOS Course, Dave Fisher
  11. Developing iPad Applications for Visualization and Insight - Carnegie Mellon University
  12. Mobile Computing - IIT Madras
  13. Mobile Information Systems - Bauhaus-Uni Weimar

Artificial Intelligence (27)

  1. CS50 - Introduction to Artificial Intelligence with Python (and Machine Learning), Harvard OCW
  2. 10-202: Introduction to Modern AI - CMU
  3. CS 188 - Introduction to Artificial Intelligence, UC Berkeley - Spring 2025, by John Canny, Oliver Grillmeyer: (Spring 2024) (Spring 2023)
  4. 6.034 Artificial Intelligence, MIT OCW
  5. CS221: Artificial Intelligence: Principles and Techniques - Autumn 2019 - Stanford University
  6. 15-780 - Graduate Artificial Intelligence, Spring 14, CMU
  7. CSE 592 Applications of Artificial Intelligence, Winter 2003 - University of Washington
  8. CS322 - Introduction to Artificial Intelligence, Winter 2012-13 - UBC: (YouTube)
  9. CS 4731/7632: Game AI, Spring 2021, Georgia Tech
  10. CS 4804: Introduction to Artificial Intelligence, Fall 2016
  11. CS 5804: Introduction to Artificial Intelligence, Spring 2015
  12. Artificial Intelligence, Fall 2023 - FAU: (Spring 2023) (Fall 2022) (Spring 2021) (Fall 2020) (Fall 2018) (Spring 2018)
  13. Artificial Intelligence - IIT Kharagpur
  14. Artificial Intelligence - IIT Madras
  15. Artificial Intelligence(Prof.P.Dasgupta) - IIT Kharagpur
  16. MOOC - Intro to Artificial Intelligence - Udacity
  17. MOOC - Artificial Intelligence for Robotics - Udacity
  18. Graduate Course in Artificial Intelligence, Autumn 2012 - University of Washington
  19. Agent-Based Systems 2015/16- University of Edinburgh
  20. Informatics 2D - Reasoning and Agents 2014/15- University of Edinburgh
  21. Artificial Intelligence - Hochschule Ravensburg-Weingarten
  22. Deductive Databases and Knowledge-Based Systems - Technische Universität Braunschweig, Germany
  23. Artificial Intelligence: Knowledge Representation and Reasoning - IIT Madras
  24. Semantic Web Technologies by Dr. Harald Sack - HPI
  25. Knowledge Engineering with Semantic Web Technologies by Dr. Harald Sack - HPI
  26. T81-558: Applications of Deep Neural Networks by Jeff Heaton, 2022, Washington University in St. Louis
  27. MSU programming for AI

Introduction to Machine Learning (124)

  1. Introduction to Machine Learning for Coders
  2. MOOC - Statistical Learning, Stanford University
  3. Statistical Learning with Python - Stanford Online
  4. Foundations of Machine Learning Boot Camp, Berkeley Simons Institute
  5. CS 155 - Machine Learning & Data Mining, 2023 - Caltech: (Notes-2020) (YouTube-2020) (Notes-2019) (YouTube-2019) (Notes-2018) (YouTube-2018) (Notes-2017) (YouTube-2017) (Notes-2016) (YouTube-2016)
  6. CS 156 - Learning from Data, Caltech
  7. 10-601 - Introduction to Machine Learning (MS) - Tom Mitchell - 2015, CMU: (YouTube)
  8. 10-601 Machine Learning | CMU | Fall 2017
  9. 10-701 - Introduction to Machine Learning (PhD) - Tom Mitchell, Spring 2011, CMU: (Fall 2014) (Spring 2015 by Alex Smola) (Fall 2020 by Ziv Bar-Joseph, Eric Xing)
  10. 6.036 - Machine Learning, Broderick - MIT Fall 2020
  11. Mediterranean Machine Learning summer school 2024: (YouTube-2023) (YouTube-2022) (YouTube-2021)
  12. LxMLS Lisbon Machine Learning School 2024: (YouTube-2023) (YouTube-2022) (YouTube-2021) (YouTube-2020)
  13. Applied Machine Learning (Cornell Tech CS 5787, Fall 2020)
  14. Stanford CS229: Machine Learning Course: (Summer 2019) (Spring 2022) (Spring 2026)
  15. CMS 165 Foundations of Machine Learning - 2019 - Caltech: (Youtube)
  16. CMS 165 Foundations of Machine Learning and Statistical Inference - 2020 - Caltech
  17. Microsoft Research - Machine Learning Course
  18. CS 446 - Machine Learning, Fall 2016, UIUC
  19. CS 582 - Machine Learning for Bioinformatics, Fall 2024, UIUC
  20. CMPUT 267 Machine Learning - Fall 2024 - University of Alberta: (Youtube)
  21. ECE 364 - Programming Methods for Machine Learning, Spring 2025, UIUC
  22. EML 4930/5930 - Machine Learning: Introduction and Application, Fall 2025, FSU
  23. undergraduate machine learning at UBC 2012, Nando de Freitas
  24. CS 229 - Machine Learning - Stanford University: (Autumn 2018)
  25. CSE 151A Introduction to Machine Learning, Prof Jingbo Shang - UCSD
  26. CS 189/289A Introduction to Machine Learning, Prof Jonathan Shewchuk - UCBerkeley
  27. CS 189/289A: Intro to Machine Learning - UC Berkeley - Spring 2026
  28. CPSC 340: Machine Learning and Data Mining (2018) - UBC
  29. CS391L Machine Learning, Spring 2025 - UT Austin
  30. CS4780/5780 Machine Learning, Fall 2013 - Cornell University
  31. CS4780/5780 Machine Learning, Fall 2018 - Cornell University: (Youtube)
  32. CSE474/574 Introduction to Machine Learning - SUNY University at Buffalo
  33. CS 5350/6350 - Machine Learning, Spring 2024, University of Utah: (Youtube)
  34. ECE 4252/8803 Fundamentals of Machine Learning (FunML), Spring 2024 - Georgia Tech
  35. ECE 5984 Introduction to Machine Learning, Spring 2015 - Virginia Tech
  36. CSx824/ECEx242 Machine Learning, Bert Huang, Fall 2015 - Virginia Tech
  37. STA 4273H - Large Scale Machine Learning, Winter 2015 - University of Toronto
  38. CSC 2515 Introduction to Machine Learning, Amir-massoud Farahmand, Fall 2021, University of Toronto
  39. ECE 421 Introduction to Machine Learning, Amir Ashouri, Winter 2019, University of Toronto
  40. EECS 4404E/5327 Introduction to Machine Learning, Amir Ashouri, Fall 2019, York University
  41. CS 480/680 Introduction to Machine Learning, Gautam Kamath, University of Waterloo: (Spring 2021)
  42. CS 480/680 Introduction to Machine Learning, Kathryn Simone, University of Waterloo: (Fall 2024)
  43. CS 485/685 Machine Learning, Shai Ben-David, University of Waterloo
  44. STAT 441/841 Classification Winter 2017 , Waterloo
  45. 10-605 - Machine Learning with Large Datasets, Fall 2016 - CMU
  46. Information Theory, Pattern Recognition, and Neural Networks - University of Cambridge
  47. Pattern Analysis (2018) - FAU: (Class 2017) (Class 2016) (Class 2015) (Class 2009)
  48. Pattern Recognition (2020-2021) - FAU: (Class 2012-2013)
  49. Beyond the Patterns (2020-2021) - FAU
  50. Python and machine learning - Stanford Crowd Course Initiative
  51. MOOC - Machine Learning Part 1a - Udacity/Georgia Tech: (Part 1b Part 2 Part 3)
  52. Pattern Recognition Class (2012)- Universität Heidelberg
  53. Introduction to Machine Learning and Pattern Recognition - CBCSL OSU
  54. Introduction to Machine Learning - IIT Kharagpur
  55. Introduction to Machine Learning - IIT Madras
  56. Pattern Recognition - IISC Bangalore
  57. Pattern Recognition and Application - IIT Kharagpur
  58. Pattern Recognition - IIT Madras
  59. Machine Learning Summer School 2013 - Max Planck Institute for Intelligent Systems Tübingen
  60. Machine Learning - Professor Kogan (Spring 2016) - Rutgers
  61. CS273a: Introduction to Machine Learning: (YouTube)
  62. Machine Learning Crash Course 2015
  63. COM4509/COM6509 Machine Learning and Adaptive Intelligence 2015-16
  64. Introduction to Machine Learning - Spring 2018 - ETH Zurich
  65. Machine Learning - Pedro Domingos- University of Washington
  66. CSE 446/546 - Machine Learning, Spring 2020 - University of Washington: (Videos)
  67. Machine Learning (COMP09012)
  68. Probabilistic Machine Learning 2020 - University of Tübingen
  69. Statistical Machine Learning 2020 - Ulrike von Luxburg - University of Tübingen
  70. COMS W4995 - Applied Machine Learning - Spring 2020 - Columbia University
  71. Introductory Applied Machine Learning - Edinburgh University
  72. Machine Learning for Engineers 2022: (YouTube)
  73. 10-418 / 10-618 (Fall 2019) Machine Learning for Structured Data
  74. ORIE 4741/5741: Learning with Big Messy Data - Cornell
  75. Machine Learning in IoT
  76. Stanford CS229M: Machine Learning Theory - Fall 2021
  77. Intro to Machine Learning and Statistical Pattern Classification - Prof Sebastian Raschka
  78. CMU's Multimodal Machine Learning course (11-777), Fall 2020
  79. EE104: Introduction to Machine Learning - Stanford University
  80. CPSC 330: Applied Machine Learning (2020) - UBC
  81. Machine Learning 2013 - Nando de Freitas, UBC
  82. Machine Learning, 2014-2015, University of Oxford
  83. 10-702/36-702 - Statistical Machine Learning - Larry Wasserman, Spring 2016, CMU: (Spring 2015)
  84. 10-715 Advanced Introduction to Machine Learning - CMU: (YouTube)
  85. CS 281B - Scalable Machine Learning, Alex Smola, UC Berkeley
  86. 100 Days of Machine Learning - CampusX (Hindi)
  87. CampusX Data Science Mentorship Program 2022-23 (Hindi)
  88. Statistical Machine Learning - S2023 - Benyamin Ghojogh
  89. MIT 6.5940 EfficientML.ai Lecture, Fall 2023
  90. TinyML - Tiny Machine Learning at UPenn
  91. ECE 4760 (Digital Systems Design Using Microcontrollers) at Cornell for the Fall, 2022: (Spring 2021)
  92. SFU CMPT 727 Statistical Machine Learning, by Maxwell Libbrecht: (Spring 2023) (Spring 2022)
  93. UC Berkeley CS 189 / 289A Introduction to Machine Learning fall 2023, by Jennifer Listgarten & Jitendra Malik
  94. UC Berkeley CS 189 Introduction to Machine Learning (CDSS offering) spring 2022, by Marvin Zhang
  95. UC San Diego/edX DSE 220X Machine Learning Fundamentals, by Sanjoy Dasgupta
  96. MIT 6.036 Introduction to Machine Learning spring 2019, by Leslie Kaelbling
  97. LMU Munich Introduction to Machine Learning
  98. CMU 15 388 / 15 688 Practical Data Science, by Zico Kolter: (Fall 2019) (Spring 2018)
  99. UW Madison CS 320 Data Programming II spring 2021, by Tyler R. Caraza-Harter
  100. UC San Diego COGS9 Introduction to Data Science fall 2020, by Jason Fleischer
  101. UCLA Stats 15 Introduction to Data Science fall 2022, by Miles Chen
  102. UCLA Stats 21 Python and Other Technologies for Data Science spring 2024, by Miles Chen: (Spring 2021)
  103. UCLA Stats C161/C261 Introduction to Pattern Recognition and Machine Learning winter 2024, by Arash Amini: (Winter 2023)
  104. UCLA Stats 231C Theories of Machine Learning spring 2022, by Arash Amini
  105. Princeton COS 511 Theoretical Machine Learning spring 2026, by Elad Hazan
  106. MSU Machine Learning
  107. Data Science for Dynamical Systems, by Oliver Wallscheid & Sebastian Peitz: (YouTube)
  108. Cambridge Statistical Learning in Practice 2021, by Alberto J. Coca
  109. Data 8: The Foundations of Data Science - UC Berkeley: (Spring 23) (Fall 22) (Spring 22) (Summer 17)
  110. Data 144: Foundations of Data Science spring 2021 - Vassar College: (Course materials)
  111. CSE519 - Data Science Fall 2016 - Skiena, SBU
  112. CS 109 Data Science, Harvard University: (YouTube)
  113. 6.0002 Introduction to Computational Thinking and Data Science - MIT OCW
  114. Data 100: Principles and Techniques of Data Science - UC Berkeley: (Fall 25) (Fall 24) (Spring 24) (Summer 19)
  115. Data 102 - Spring 21- UC Berkeley: (YouTube)
  116. Distributed Data Analytics (WT 2017/18) - HPI University of Potsdam
  117. Data Profiling and Data Cleansing (WS 2014/15) - HPI University of Potsdam
  118. CS 229r - Algorithms for Big Data, Harvard University: (Youtube)
  119. Algorithms for Big Data - IIT Madras
  120. Python Data Science with the TCLab: (YouTube)
  121. Foundations of Data Analysis (Fall 2020)- University of Utah
  122. Introduction to Data Science (IDS) - Wil van der Aalst (RWTH Aachen University): (2021-2022 edition)
  123. Machine Learning Class (Winter 2019-2020) - UniHeidelberg
  124. Toronto ECE 1513 Introduction to Machine Learning winter 2026, by Ali Bereyhi

Data Mining (18)

  1. CSEP 546, Data Mining - Pedro Domingos, Sp 2016 - University of Washington: (YouTube)
  2. CS 5140/6140 - Data Mining, Spring 2020, University of Utah by Prof. Jeff Phillips: (Youtube)
  3. CS 5140/6140 - Data Mining, Spring 2023, University of Utah by Prof. Ana Marasović: (Youtube)
  4. CS 5955/6955 - Data Mining, University of Utah: (YouTube)
  5. Statistics 202 - Statistical Aspects of Data Mining, Summer 2007 - Google: (YouTube)
  6. DSC 253/CSE 261 - Advanced Data-Driven Text Mining, Winter 2026, UC San Diego by Prof. Jingbo Shang
  7. MOOC - Text Mining and Analytics by ChengXiang Zhai
  8. Information Retrieval SS 2014, iTunes - HPI
  9. MOOC - Data Mining with Weka
  10. CS 290 DataMining Lectures
  11. CS246 - Mining Massive Data Sets, Winter 2016, Stanford University: (YouTube)
  12. Information Retrieval - Spring 2018 - ETH Zurich
  13. Information Retrieval - WS 2022/23 - Universität Freiburg
  14. CAP6673 - Data Mining and Machine Learning - FAU: (Video lectures)
  15. DSC 148 - Introduction to Data Mining - UCSD
  16. CS 412 - Introduction to Data Mining - UIUC
  17. CS 512 - Data Mining Principles - UIUC: (YouTube)
  18. MGTA 415 - Analyzing Unstructured Data - UCSD

Probabilistic Graphical Modeling (4)

  1. CS 6190 - Probabilistic Modeling, Spring 2016, University of Utah
  2. 10-708 - Probabilistic Graphical Models, Carnegie Mellon University
  3. Probabilistic Graphical Models, Daphne Koller, Stanford University
  4. Probabilistic Graphical Models, Spring 2018 - Notre Dame

Deep Learning (59)

  1. Full Stack Deep Learning - Course 2022
  2. Full Stack Deep Learning - Course 2021
  3. NYU Deep Learning Spring 2020
  4. NYU Deep Learning Spring 2021
  5. 6.S191: Introduction to Deep Learning - MIT
  6. 15.773: Hands-On Deep Learning Spring 2024 - MIT
  7. 6.7960: Deep Learning Fall 2024 - MIT
  8. Intro to Deep Learning and Generative Models Course - Prof Sebastian Raschka
  9. Deep Learning CMU
  10. CS231n Deep Learning for Computer Vision - Stanford University: (Spring 2025) (Winter 2016 Andrej Karpathy)
  11. Deep Learning: CS 182 Spring 2021
  12. 10-414/714: Deep Learning Systems - CMU: (Youtube)
  13. 11-785: Introduction to Deep Learning - CMU: (Lectures - YouTube-2024, Recitations - YouTube-2024)
  14. Part 1: Practical Deep Learning for Coders, v3 - fast.ai
  15. Part 2: Deep Learning from the Foundations - fast.ai
  16. Deep learning at Oxford 2015 - Nando de Freitas
  17. Self-Driving Cars, Andreas Geiger, 2021/22: (YouTube)
  18. 6.S094: Deep Learning for Self-Driving Cars - MIT
  19. 6.S985: How to AI Almost Anything/Multimodal AI - MIT
  20. CS294-129 Designing, Visualizing and Understanding Deep Neural Networks: (YouTube)
  21. CS230: Deep Learning - Autumn 2018 - Stanford University
  22. CS230: Deep Learning - Autumn 2025 - Stanford University
  23. STAT-157 Deep Learning 2019 - UC Berkeley
  24. Deep Learning, Stanford University
  25. MOOC - Neural Networks for Machine Learning, Geoffrey Hinton 2016 - Coursera
  26. Stat 946 Deep Learning - University of Waterloo
  27. EECS 298 Theory of Computational Neural Networks and Machine Learning (Fall 2020) - UC Irvine: (YouTube)
  28. ECE 1508 Applied Deep Learning - University of Toronto: (Fall 2025) (Winter 2025) (Fall 2024)
  29. ECE 1508 Reinforcement Learning - Fall 2025 - University of Toronto
  30. Neural networks class - Université de Sherbrooke: (YouTube)
  31. DLCV - Deep Learning for Computer Vision - UPC Barcelona
  32. DLAI - Deep Learning for Artificial Intelligence @ UPC Barcelona
  33. Neural Networks and Applications - IIT Kharagpur
  34. UVA DEEP LEARNING COURSE
  35. Deep Learning - Winter 2020-21 - Tübingen Machine Learning
  36. Geometric Deep Learning - AMMI
  37. Math for Deep Learning, Andreas Geiger
  38. Applied Deep Learning 2025 - TU Wien: (2024) (2023) (2022) (2021) (2020)
  39. Neural Networks: Zero to Hero - Andrej Karpathy
  40. CIS 522 - Deep Learning - U Penn
  41. Deep Learning (Fall 2020) - FAU: (Spring 2020) (Fall 2019) (Spring 2019) (Fall 2018) (Spring 2018)
  42. Deep Learning (Fall 2020) - Georgia Tech
  43. Mathematics of Deep Learning (2021) - FAU
  44. CS7015 - Deep Learning - Prof. Mitesh M. Khapra - IIT Madras
  45. ETH Zürich | Deep Learning in Scientific Computing 2023
  46. Deep Learning François Fleuret
  47. Applied Deep Learning Maziar Raissi
  48. UC Berkeley CS 182 / 282a Deep Learning spring 2023, by Anant Sahai
  49. CMSC 828W Foundations of Deep Learning (Fall 2020) - UMD: (YouTube)
  50. TUM IN2346 Introduction to Deep Learning Fall 2024, by Daniel Cremers: (Summer 2023)
  51. UT Austin - Advances in Deep Learning
  52. HKU - Data 8014 Principles of Deep Representation Learning Fall 2025, by Yi Ma
  53. Local Explanations for Deep Learning Models (Fall 2023)
  54. CS 6966/5966 Interpretability of LLMs (Spring 2026) - University of Utah
  55. Deep Graph Learning (DGL, 2025) - Prof. Islem Rekik - Imperial College London
  56. Intuitive Deep Learning Series
  57. Toronto ECE 1508 Applied Deep Learning winter 2026, by Ali Bereyhi: Youtube
  58. UC Berkeley CS 182 / 282a Deep Learning fall 2025, by Gireeja Ranade & Anant Sahai: Youtube
  59. SNU M2177.43 Introduction to Deep Learning spring 2026, by Hyun Oh Song

Reinforcement Learning (35)

  1. CS234: Reinforcement Learning - Spring 2024 - Stanford University: (Winter 2019)
  2. CSE 542: Reinforcement Learning - Spring 2024 - University of Washington
  3. CSE 579: Reinforcement Learning - Autumn 2024 - University of Washington
  4. CSC 2547: Introduction to Reinforcement Learning - Spring 2021 - University of Toronto: (YouTube)
  5. Introduction to reinforcement learning - UCL
  6. Reinforcement Learning - IIT Madras: (TA - Manav Mishra, TA - Prabhleen Kukreja, TA - Sandarbh Yadav , TA - Avik Kar)
  7. Special topics in ML (Reinforcement Learning) IIT madras
  8. CS885 Reinforcement Learning - Spring 2018 - University of Waterloo
  9. CS 224R - Deep Reinforcement Learning- Stanford: (YouTube)
  10. CS 285 - Deep Reinforcement Learning- UC Berkeley: (Spring 2026) (Fall 2019)
  11. CS 294 112 - Reinforcement Learning
  12. NUS CS 6101 - Deep Reinforcement Learning
  13. ECE 8851: Reinforcement Learning
  14. CS294-112, Deep Reinforcement Learning Sp17: (YouTube)
  15. UCL Course 2015 on Reinforcement Learning by David Silver from DeepMind: (YouTube)
  16. DeepMind x UCL Reinforcement Learning Lecture Series 2021 - Hado van Hasselt, Diana Borsa, Matteo Hessel
  17. Deep RL Bootcamp - Berkeley Aug 2017
  18. Reinforcement Learning Course at KTH (FDD3359 - 2022)
  19. Reinforcement Learning Course at ASU, Spring 2022
  20. CS 4789/5789: Introduction to Reinforcement Learning - Cornell
  21. S20/IE613 - Online (Machine) Learning/ Bandit Algorithms
  22. Reinforcement Learning - Fall 2021 chandar-lab
  23. ECE524 Foundations of Reinforcement Learning at Princeton University, Spring 2024
  24. REINFORCEMENT LEARNING AND OPTIMAL CONTROL - Dimitri P. Bertsekas, ASU
  25. CMU 16 745 Optimal Control and Reinforcement Learning spring by Zac Manchester
  26. CMU 16 899 Adaptive Control and Reinforcement Learning fall 2020, by Changliu Liu
  27. Jadavpur University, 2025: Introduction to Reinforcement Learning
  28. EE675 (2024) Introduction to Reinforcement Learning Course | IIT Kanpur
  29. Reinforcement Learning Course by Frédéric Godin - Concordia University
  30. CS 285: Deep RL, 2023
  31. Mathematical Foundations of Reinforcement Learning - WINDY Lab
  32. Reinforcement Learning (HMC CS 181V)-Spring, 2020 - Neil Rhodes
  33. Reinforcement Learning Course: Lectures (Summer 2023) by Paderborn University
  34. CS292F (Spring 2021) Statistical Foundation of Reinforcement Learning - UCSD
  35. Algorithmic Foundations of Interactive Learning - CMU

Advanced Machine Learning (11)

  1. Advanced Machine Learning, 2021-2022, Sem I - by Prof. Madhavan Mukund, CMI
  2. 18.409 Algorithmic Aspects of Machine Learning Spring 2015 - MIT
  3. CS 330 - Deep Multi-Task and Meta Learning - Fall 2019 - Stanford University: (Youtube)
  4. Stanford CS330: Deep Multi-Task and Meta Learning I Autumn 2022
  5. ES 661 (2023): Probabilistic Machine Learning - IIT Gandhinagar
  6. Information Retrieval in High Dimensional Data
  7. Trustworthy Machine Learning - Winter Semester 2023-2024, University of Tübingen
  8. Trustworthy Machine Learning - Winter Semester 2024-2025, University of Tübingen
  9. ETH Zürich Advanced Machine Learning fall 2019, by Joachim M. Buhmann
  10. CS 159 Advanced Topics in Machine Learning, Spring 2021 - Caltech
  11. CS 229br Advanced Topics in the theory of machine learning, Spring 2021 - Harvard

Natural Language Processing (26)

  1. CS 224N -Natural Language Processing with Deep Learning - Stanford University: (Lectures - Winter 2019) (Lectures - Winter 2021) (Lectures - Spring 2024)
  2. CS 224N - Natural Language Processing, Stanford University: (Lecture videos)
  3. Stanford XCS224U: Natural Language Understanding I Spring 2023
  4. CS388: Natural Language Processing - UT Austin
  5. CS 124 - From Languages to Information - Stanford University
  6. CS 6340/5340 - Natural Language Processing - University of Utah - Spring 2024: (Youtube)
  7. CSE 447/517 - Natural Language Processing - University of Washington - Winter 2024
  8. Neural Networks: Zero to Hero - Andrej Karpathy
  9. fast.ai Code-First Intro to Natural Language Processing: (Github)
  10. MOOC - Natural Language Processing - Coursera, University of Michigan
  11. Natural Language Processing at UT Austin (Greg Durrett)
  12. CS224U: Natural Language Understanding - Spring 2019 - Stanford University
  13. Deep Learning for Natural Language Processing, 2017 - Oxford University
  14. Natural Language Processing - IIT Bombay
  15. CMU Advanced NLP Fall 2024: (Lectures - Fall 2024) (Lectures - Fall 2021)
  16. CMU Neural Nets for NLP 2021
  17. Natural Language Processing - Michael Collins - Columbia University
  18. CMU CS11-711 - Advanced Natural Language Processing: (Lectures - Spring 2025)
  19. CMU CS11-737 - Multilingual Natural Language Processing
  20. UMass CS685: Advanced Natural Language Processing (Spring 2022)
  21. Natural Language Processing (CMSC 470)
  22. Stanford CS25 - Transformers United 2023
  23. Natural Language Processing (IN2361) - TUM
  24. Natural Language Processing (Spring 2024) - University of Utah
  25. Speech Technology - IIT Madras
  26. Deep Learning for NLP (KAIST AI605 Fall 2021)

Generative AI and LLMs (37)

  1. Stanford CS236: Deep Generative Models I 2023 I Stefano Ermon
  2. CS 6785 - Deep Generative Models - Cornell Tech, Spring 2023)
  3. ECE 498 / 598 - Deep Generative Models - UIUC, Fall 2025)
  4. Mathematical Foundations of Generative AI - IIT Madras
  5. Deep Generative Models - IISC
  6. A Course on Generative AI - Diffusion Models - Israel Institute of Technology
  7. MIT 6.S184 Flow Matching and Diffusion Models, 2025
  8. Course on Diffusion Models for Generative AI - UT Austin
  9. CS 492(C) Diffusion and Flow Models - Fall 2025 - KAIST: (YouTube)
  10. Stanford CS329A Self-Improving AI Agents 2025 - Stanford: (YouTube)
  11. Stanford CS336 Language Modeling from Scratch I 2025 - Stanford: (2026)
  12. Stanford CME295 Transformers & LLMs - Autumn 2025 - Stanford
  13. Stanford CME296: Diffusion & Large Vision Models
  14. Introduction to large language models - IIT Madras
  15. Build a Large Language Model (From Scratch) by Sebastian Raschka
  16. Reinforcement Learning of Large Language Models - UCLA
  17. WING NUS CS6101 Large Language Models (T2310)
  18. CS 839: Foundation Models Fall 2025 - UW Madison: (YouTube playlists)
  19. CS 886: Recent Advances on Foundation Models Winter 2024 - University of Waterloo
  20. UC Berkeley CS 194/294-196 Agentic AI Fall 2025: (YouTube, 2025) (Notes, 2024) (YouTube, 2024)
  21. UC Berkeley CS 194/294-267 Understanding Large Language Models Foundations and Safety spring 2024, by Dawn Song & Dan Hendrycks
  22. UC Berkeley CS 194/294-280 Advanced Large Language Model Agents Spring 2025: (YouTube)
  23. Introduction to Large Language Models (LLMs), IIT Delhi
  24. CMU 10423 S24: Generative AI: Videos
  25. CMU 10799 S26: Diffusion & Flow Matching
  26. 6.S183: A Practical Introduction to Diffusion Models - MIT: (YouTube, 2025 lectures)
  27. The Principles of Diffusion Models - Vizuara
  28. Building LLMs from scratch - Vizuara
  29. Build DeepSeek from Scratch - Vizuara
  30. Reasoning LLMs from Scratch - Vizuara
  31. KAIST CS492(C): Diffusion and Flow Models (Fall 2025): (2024)
  32. Mathematics of Generative Modelling (Spring 2024) - IIIT Hyderabad: (2023)
  33. NYCU Deep Generative Models 2025
  34. COMPSCI 690L | Deep Generative Models | 2024 | Sajjad Amini
  35. ECE1508: Deep Generative Models - Summer 2025 - UofT
  36. 6.S980 Machine Learning for Inverse Graphics - Fall 2022 - MIT
  37. 6.S087 - Foundation Models and Generative AI - MIT

Computer Vision (18)

  1. CS 231n - Convolutional Neural Networks for Visual Recognition, Stanford University
  2. The Ancient Secrets of Computer Vision, University of Washington - Joseph Redmon: (YouTube)
  3. CS 198-126: Modern Computer Vision Fall 2022 (UC Berkeley)
  4. Machine Learning for Robotics and Computer Vision, WS 2013/2014 - TU München: (YouTube)
  5. COGSCI 1 - Intro to Cognitive Science Summer 2022 - UC Berkeley
  6. Informatics 1 - Cognitive Science 2015/16- University of Edinburgh
  7. Informatics 2A - Processing Formal and Natural Languages 2016-17 - University of Edinburgh
  8. NOC:Deep Learning For Visual Computing - IIT Kharagpur
  9. Extreme Classification
  10. EECS 498/598 - Deep Learning for Computer Vision - University of Michigan - Fall 2019: (Youtube)
  11. Computer Vision - FAU Spring 2021: (Spring 2018)
  12. CAP5415 Computer Vision - UCF Fall 2023
  13. CAP6412 Advanced Computer Vision - UCF Spring 2024: (Youtube)
  14. CU Boulder CSCI 5722 Computer Vision - CU Boulder Spring 2025: (Youtube)
  15. Advanced Deep Learning for Computer vision (ADL4CV) (IN2364) - TU Munich: (Youtube)
  16. Advanced Deep Learning for Physics (ADL4P) - TU Munich
  17. Computer Vision III: Detection, Segmentation and Tracking (CV3DST) (IN2375) - TU Munich
  18. Lecture: Computer Vision, University of Tübingen - Andreas Geiger: (YouTube)

Time Series Analysis (2)

  1. 02417 Time Series Analysis
  2. Applied Time Series Analysis

Optimization (28)

  1. Optimisation for Machine Learning: Theory and Implementation (Hindi) - IIT
  2. Rochester DSCC 435 Optimization for Machine Learning fall 2023, by Jiaming Liang
  3. Princeton ELE539/COS512 Optimization for Machine Learning spring 2021, by Chi Jin
  4. UT Dallas CS 7301 Advanced Topics in Optimization for Machine Learning spring 2021, by Rishabh Iyer: (YouTube)
  5. Convex Analysis, Summer 2021 - TU Braunschweig: (YouTube)
  6. EE364a: Convex Optimization I - Stanford University
  7. EE364b: Convex Optimization II - Stanford University: (YouTube)
  8. 10-725 Convex Optimization, Spring 2015 - CMU
  9. 10-725 Convex Optimization: Fall 2016 - CMU
  10. 10-725 Optimization Fall 2012 - CMU
  11. 10-801 Advanced Optimization and Randomized Methods - CMU: (YouTube)
  12. AM 207 - Stochastic Methods for Data Analysis, Inference and Optimization, Harvard University
  13. MIT 6.S098 Applied Convex Optimization IAP 2022, by Alexandre Amice, Benoit Legat: (YouTube)
  14. UVic Math 428 / 529 Discrete Optimization fall 2025, by Jonathan A. Noel: (YouTube)
  15. University of Twente Discrete Optimization, by Marc Uetz: (Fall 2020)
  16. UC Davis MAT 168 Optimization winter 2024, by Matthias Köppe
  17. Purdue University CHE 597 Computational Optimization spring 2025, by Can Li
  18. Purdue University ECE 695 Optimization for Deep Learning (OPT4DL), by Abolfazl Hashemi: YouTube - Fa 25
  19. UCSD CS292F Convex Optimization Spring 2020, by Yu-Xiang Wang: (Youtube)
  20. UIUC ECE 490 Introduction to Optimization fall 2020, by Venugopal V. Veeravalli: (YouTube)
  21. University of Wisconsin-Madison CS/ECE/ISyE 524 Introduction to Optimization spring 2017-18, by Laurent Lessard
  22. University of Wisconsin-Madison ISyE/Math/CS/Stat 525 Linear Optimization fall 2021, by Alberto Del Pia
  23. University of Wisconsin-Madison ISyE/Math/CS 728 Integer Optimization (second part of the course) spring 2020
  24. Columbia IEOR E4007 Optimization Models and Methods 2005, by Garud Iyengar
  25. UC Berkeley EECS 127 / 227A Optimization Models in Engineering: (Spring 2024 lectures)
  26. Modern Optimization and theory for Deep Learning
  27. Manifold Learning, Optimization and Information Geometry - Politecnico Milan - PhD level
  28. Robust Optimization and Essentials of Numerical Nonsmooth Optimization - EUROPT Summer School 2021

Unsupervised Learning (8)

  1. CS294 Deep Unsupervised Learning Spring 2024
  2. Deep Unsupervised Learning -- Berkeley Spring 2020
  3. CS294-158 Deep Unsupervised Learning SP19
  4. UC San Diego COGS 118A Supervised Machine Learning fall 2020, by Jason Fleischer
  5. UC San Diego COGS 118B Unsupervised Machine Learning winter 2024, by Jason Fleischer
  6. UIUC STAT 437 Unsupervised Learning spring 2024, by Tori Ellison
  7. Johns Hopkins Unsupervised Learning spring 2017, by Rene Vidal
  8. Unsupervised Learning (STAT 841), Winter 2017

Misc Machine Learning Topics (40)

  1. Quantum Machine Learning | 2021 Qiskit Global Summer School
  2. CS 6955 - Clustering, Spring 2015, University of Utah
  3. Info 290 - Analyzing Big Data with Twitter, UC Berkeley school of information: (YouTube)
  4. CS224W Machine Learning with Graphs | Spring 2021 | Stanford University
  5. 9.520 - Statistical Learning Theory and Applications, Fall 2015 - MIT
  6. EE531 - Statistical Learning Theory, Spring 2026 - KAIST
  7. Statistical Learning Theory, Spring 2019 - ETH Zürich
  8. Course on the Statistical Learning Theory, University of São Paulo, ICMC
  9. Reinforcement Learning - UCL
  10. Regularization Methods for Machine Learning 2016: (YouTube)
  11. Statistical Inference in Big Data - University of Toronto
  12. Reinforcement Learning - IIT Madras
  13. Statistical Rethinking Winter 2015 - Richard McElreath
  14. Foundations of Machine Learning - Blmmoberg Edu
  15. Web Information Retrieval (Proff. L. Becchetti - A. Vitaletti)
  16. Big Data Systems (WT 2019/20) - Prof. Dr. Tilmann Rabl - HPI
  17. Introduction to Data-Centric AI - MIT
  18. Parallel Computing and Scientific Machine Learning
  19. Machine Learning System Design - System Design Fight Club
  20. CS 329S - Machine Learning Systems Design, Winter 2022 - Chip Huyen, Stanford University: (Syllabus & lecture videos)
  21. UT Austin ECE 381V Bandits and Online Learning fall 2021, by Sanjay Shakkottai
  22. UCSD MATH 273B Information Geometry and its Applications winter 2022, by Melvin Leok
  23. Cornell ECE 5545 Machine Learning Hardware and Systems Spring 2022, by Mohamed Abdelfattah
  24. High Dimensional Analysis: Random Matrices and Machine Learning by Roland Speicher: (Youtube)
  25. ACP SUMMER SCHOOL 2023 on Machine Learning for Constraint Programming
  26. EE512A - Advanced Inference in Graphical Models, Fall Quarter, 2014
  27. University of Wisconsin-Madison CS/ECE 561 - Probability and Information Theory in Machine Learning fall 2020, by Matthew Malley
  28. University of Maryland CMSC828U Algorithms in Machine Learning: Guarantees and Analyses fall 2020, by Furong Huang: (YouTube playlist)
  29. Statistical Physics of Machine Learning
  30. 11-755 - Machine Learning for Signal Processing, CMU: (YouTube-2024, YouTube-2023)
  31. Machine Learning for 3D Data, Fall 2023, KAIST
  32. Machine Learning for 3D Data, Spring 2026, KAIST: Recordings
  33. Machine Learning for Physicists, Spring 2019, FAU: (Spring 2017)
  34. CSCE 585 - Machine Learning Systems, University of South Carolina: (YouTube-2020)
  35. CS-E4740 - Federated Learning, Spring 2023, Aalto University
  36. Uncertainty Modeling in AI | National University of Singapore
  37. Process Mining Course @ RWTH Aachen University (BPI 2021)
  38. CS 2881 AI Safety - Harvard - Fall 2025: (Youtube)
  39. Chat GPT-X and Generative Models for EM Systems Design - KAIST
  40. CS492(F) Computational Learning Theory (Fall 2021, KAIST)

Computer Networks (30)

  1. CS 144 Introduction to Computer Networking - Stanford University, Fall 2013: (Lecture videos)
  2. Computer Networking: A Top-Down Approach
  3. Computer Communication Networks, Rensselaer Polytechnic Institute - Fall 2001: (Videos) (Slides)
  4. Audio/Video Recordings and Podcasts of Professor Raj Jain's Lectures - Washington University in St. Louis: (YouTube)
  5. Computer Networks, Tanenbaum, Wetherall Computer Networks 5e - Video Lectures
  6. CSEP 561 - PMP Network Systems, Fall 2013 - University of Washington: (Videos)
  7. CSEP 561 – Network Systems, Autumn 2008 - University of Washington: (Videos)
  8. ECE/CS 438 - Communication Networks, Fall 2020 - UIUC
  9. Computer Networks - IIT Kharagpur
  10. Introduction to Data Communications 2013, Steven Gordon - Thammasat University, Thailand
  11. Introduction to Complex Networks - RIT
  12. Computer Networks and Internet Protocol - IIT Kharagpur
  13. Structural Analysis and Visualization of Networks
  14. Columbia ELEN E4703 Wireless Communications spring 2006, by Angel Lozano
  15. Columbia COMS W4119 Computer Networks fall 2004, by Vishal Misra
  16. Columbia ELEN E4710 An Introduction to Network Engineering fall 2004, by Dan Rubenstein: (Videos)
  17. Data Communication - IIT Kharagpur
  18. Error Correcting Codes - IISC Bangalore
  19. Information Theory and Coding - IIT Bombay
  20. Complex Network : Theory and Application - IIT Kharagpur
  21. Advanced 3G and 4G Wireless Mobile Communications - IIT Kanpur
  22. Broadband Networks: Concepts and Technology - IIT Bombay
  23. Coding Theory - IIT Madras
  24. Digital Communication - IIT Bombay
  25. Digital Voice & Picture Communication - IIT Kharagpur
  26. Wireless Ad Hoc and Sensor Networks - IIT Kharagpur
  27. Internetworking with TCP/IP by Prof. Dr. Christoph Meinel - HPI
  28. CS798: Mathematical Foundations of Computer Networking - University of Waterloo
  29. CS 168 Introduction to the Internet: Architecture and Protocols, Fall 2022 - UC Berkeley: (YouTube - Fall 2022) (Spring 2025)
  30. Advanced Topics in Communication Networks, Fall 2022 - ETH Zürich

Math for Computer Scientist (129)

  1. Maths courses all topics covered - Khan Academy
  2. 18.01 Single Variable Calculus, Fall 2006 - MIT OCW
  3. 18.02 Multivariable Calculus, Fall 2007 - MIT OCW
  4. 18.03 Differential Equations, Spring 2010 - MIT OCW
  5. Highlights of Calculus - Gilbert Strang, MIT OCW
  6. MAT123 Introduction to Calculus (Fall 2015) - Stony Brook
  7. Vector Calculus for Engineers - HKUST
  8. 6.042J - Mathematics for Computer Science, MIT OCW
  9. 6.1200J - Mathematics for Computer Science, MIT OCW: (Spring 24 videos)
  10. 18.200 - Principles of Discrete Applied Mathematics, MIT OCW
  11. CS 2050 Discrete Mathematics, Georgia Tech: Videos - Su 2024
  12. UC Berkeley Computer Science 70, Discrete Mathematics and Probability Theory - Fall 2025: (YouTube)
  13. CSE 547 Discrete Mathematics, Prof Skiena, University of Stony Brook
  14. Discrete Structures (Summer 2011) - Rutgers, The State University of New Jersey
  15. Discrete Mathematics and Mathematical Reasoning 2015/16 - University of Edinburgh
  16. Discrete Mathematical Structures - IIT Madras
  17. Discrete Structures - Pepperdine University
  18. CMU 21 228 Discrete Mathematics spring 2021, by Po-Shen Loh
  19. COMP2804: Discrete Structures II
  20. Statistics - CrashCourse
  21. 6.041 Probabilistic Systems Analysis and Applied Probability - MIT OCW
  22. Stanford CS109 Introduction to Probability for Computer Scientists I 2022 I Chris Piech
  23. MIT RES.6-012 Introduction to Probability, Spring 2018 - MIT
  24. Statistics 110 - Probability - Harvard University
  25. Statistical Rethinking 2026 - Richard McElreath
  26. STAT 2.1x: Descriptive Statistics | UC Berkeley
  27. STAT 2.2x: Probability | UC Berkeley
  28. MOOC - Statistics: Making Sense of Data, Coursera
  29. MOOC - Statistics One - Coursera
  30. Probability and Random Processes - IIT Kharagpur
  31. MOOC - Statistical Inference - Coursera
  32. 131B - Introduction to Probability and Statistics, UCI
  33. STATS 250 - Introduction to Statistics and Data Analysis, UMichigan
  34. Sets, Counting and Probability - Harvard
  35. Opinionated Lessons in Statistics: (Youtube)
  36. Statistics - Brandon Foltz
  37. Statistical Rethinking: A Bayesian Course Using R and Stan: (Lectures) (Book)
  38. 02402 Introduction to Statistics E12 - Technical University of Denmark: (F17)
  39. Engineering Probability (ECSE-2500) - RPI
  40. Purdue ECE302 Introduction to Probability for Data Science
  41. Undergraduate Probability with Professor Roman Vershynin
  42. High-Dimensional Probability
  43. Mathematical Statistics - 2024: (YouTube-2020)
  44. Bayesian Data Analysis
  45. Bayesian Machine Learning and Information Processing: (YouTube-2021/22) (YouTube-2020/21)
  46. Markov Processes - Spring 2023
  47. Measure Theoretic Probability
  48. Causal Inference Course - Brady Neal
  49. Causal Inference -- Online Lectures (M.Sc/PhD Level)
  50. Machine Learning & Causal Inference: A Short Course
  51. Causal Inference Jonas Peters
  52. UIUC ECE 534 Random Processes fall 2020 - Ilan Shomorony
  53. ISyE 320 Simulation and Probabilistic Modeling spring 2022, by Qiaomin Xie - University of Wisconsin-Madison
  54. Cambridge Principles of Statistics 2020, by Alberto J. Coca
  55. UC Berkeley STAT 150 Stochastic Processes spring 2021, by Brett Kolesnik
  56. UIUC Math 564 Applied Stochastic Processes fall 2016, by Kay Kirkpatrick
  57. UCLA Stats 10 Introduction to Statistical Reasoning summer 2022, by Miles Chen
  58. UCLA Stats 101C Statistical Models and Data Mining summer 2022, by Miles Chen
  59. UCLA Stats 102A Introduction to Computational Statistics with R winter 2024, by Miles Chen
  60. UCLA Stats 102B Computation and Optimization for Statistics spring 2024, by Miles Chen
  61. UCLA Stats 102C Introduction to Monte Carlo Methods fall 2023, by Miles Chen
  62. UCLA Stats 200B Theoretical Statistics winter 2024, by Arash Amini: (Winter 2023)
  63. UCLA Stats 200C High-dimensional Statistics spring 2022, by Arash Amini: (Spring 2021)
  64. UCLA Stats 203 Large Sample Theory fall 2021, by Jingyi Jessica Li: (Fall 2020)
  65. UCSD Math 280 Probability Theory and Stochastic Processes, by Todd Kemp: (YouTube)
  66. METU EE 531 Probability and Stochastic Processes, by Elif Uysal
  67. 6.262 Discrete Stochastic Processes - MIT OCW
  68. 18.650 Statistics for Applications - MIT OCW
  69. STAT240 - Robust Statistics - UC Berkeley
  70. MAS575 Combinatorics at KAIST
  71. Probabilistic method, KAIST 2020
  72. Mathematical Foundations of Machine Learning (Fall 2021) - University of Chicago - Rebecca Willett
  73. Mathematical Foundations of Machine Learning (Fall 2025) - Rebecca Willett - University of Chicago
  74. NSF CBMS 2025 - Computational Mathematics and AI, Lars Ruthotto, Emory University: (Fall 25 videos)
  75. 18.06 - Linear Algebra, Prof. Gilbert Strang, MIT OCW
  76. 18.065 Matrix Methods in Data Analysis, Signal Processing, and Machine Learning - MIT OCW
  77. University of Wisconsin-Madison ECE/CS/ME 532 Matrix Methods in Machine Learning fall 2017, by Laurent Lessard
  78. Linear Algebra (Princeton University)
  79. MOOC: Coding the Matrix: Linear Algebra through Computer Science Applications - Coursera
  80. CS 053 - Coding the Matrix - Brown University: (Fall 14 videos)
  81. Linear Algebra Review - CMU
  82. A first course in Linear Algebra - N J Wildberger - UNSW
  83. INTRODUCTION TO MATRIX ALGEBRA
  84. Computational Linear Algebra - fast.ai: (Github)
  85. ENGR108: Introduction to Applied Linear Algebra-Vectors, Matrices, and Least Squares - Stanford University
  86. MIT 18.S096 Matrix Calculus For Machine Learning And Beyond
  87. Cornell MATH 2940 Linear Algebra for Engineers spring 2009, by Andy Ruina
  88. 10-600 Math Background for ML - CMU
  89. MIT 18.065 Matrix Methods in Data Analysis, Signal Processing, and Machine Learning
  90. Direct Methods for Sparse Linear Systems - Prof Tim Davis - UFL
  91. 36-705 - Intermediate Statistics - Larry Wasserman, CMU: (YouTube)
  92. Combinatorics - IISC Bangalore
  93. Advanced Engineering Mathematics - Notre Dame
  94. Statistical Computing for Scientists and Engineers - Notre Dame
  95. Statistical Computing, Fall 2017 - Notre Dame
  96. Statistics 243 Introduction to Statistical Computing, Fall 2015 - UC Berkeley: (Notes-2015) (YouTube-2014) (Notes-2014) (YouTube-2013)
  97. Mathematics for Machine Learning, Lectures by Ulrike von Luxburg - Tübingen Machine Learning
  98. Essential Mathematics for Machine Learning- July 2018 - IIT Roorkee - YouTube Lectures
  99. Numerics of Machine Learning (Winter 2022/23) - Tübingen Machine Learning
  100. Nonlinear Dynamics and Chaos - Steven Strogatz, Cornell University
  101. Nonlinear Dynamics & Chaos - Virginia Tech
  102. An introduction to Optimization on smooth manifolds (with book) - EPFL
  103. Math Modelling
  104. Large-Scale Convex Optimization: Algorithms & Analyses via Monotone Operators by Ernest Ryu and Wotao Yin
  105. An Overview of Variational Analysis 2021 by Tyrrell Rockafellar
  106. UW AMATH 584 Applied Linear Algebra & Numerical Analysis by Nathan Kutz
  107. UW AMATH 584 Applied Linear Algebra & Introductory Numerical Analysis fall 2005, by Loyce Adams
  108. Stanford CME 206 Introduction to Numerical Methods for Engineering spring 2005, by Charbel Farhat
  109. Stanford CME 200 Linear Algebra with Application to Engineering Computations autumn 2004, by Margot Gerritsen
  110. Stanford CME 302 Numerical Linear Algebra autumn 2007, by Gene Golub
  111. TUe Numerical Linear Algebra 2021, by Martijn Anthonissen
  112. Numerical Linear Algebra fall 2018, by Jaegul Choo
  113. MIT 6.S955 Applied Numerical Algorithms fall 2023, by Justin Solomon
  114. MIT 6.7350 Numerical Algorithms for Computing and Machine Learning fall 2025, by Justin Solomon
  115. UC Berkeley Math 54 Linear Algebra & Differential Equations spring 2022, by Alexander Paulin: .html) (Summer 2021, by Peter Koroteev) (Summer 2020, by Luvreet Sangha) (Spring 2018, by Alexander Paulin.html))
  116. UC Berkeley Math 55 Discrete Mathematics fall 2021, by Nikhil Srivastava
  117. UC Berkeley Math 56 Linear Algebra fall 2023, by Alexander Paulin: .html)
  118. Fundamental Mathematics for Robotics spring 2020, by Ken Tomiyama
  119. Short Course on Casual Inference, by Sanjay Shakkottai
  120. UCLA STAT 100C Linear Models spring 2023, by Arash Amini
  121. MSU Math for Computing
  122. Mathematics of Data Science - ETH Zurich
  123. Mathematical Data Science 1 - Spring 2021 - FAU: (Spring 2020)
  124. Engineering Mathematics (UW ME564 and ME565) - Steve Brunton
  125. Beginning Scientific Computing - Steve Brunton
  126. Jadavpur University: Foundation_Math_forML_Autumn23
  127. FAU: Inverse Problems Autumn21
  128. EdX 1st Course on SparseLand by Michael Elad - Theory
  129. EdX 2nd Course on SparseLand by Michael Elad - Practice

Web Programming and Internet Technologies (16)

  1. CS50's Web Programming with Python and JavaScript
  2. Web Design Decal - HTML/CSS/JavaScript Course, University of California, Berkeley
  3. CS 75 Building Dynamic Websites - Harvard University
  4. Internet Technology - IIT Kharagpur
  5. Introduction to Modern Application Development - IIT Madras
  6. CSE 199 - How the Internet Works, Fall 2016 - University of Buffalo
  7. Open Sourced Elective: Database and Rails - Intro to Ruby on Rails, University of Texas: (Lectures - Youtube)
  8. CSE154 - Web Programming, Spring 2020 - University of Washington: (Videos)
  9. CSEP545 - Transaction Processing for E-Commerce, Winter 2012 - University of Washington: (Videos)
  10. CT 310 Web Development - Colorado State University
  11. Internet Technologies and Applications 2012, Steven Gordon - Thammasat University, Thailand
  12. CSCI 3110 Advanced Topics in Web Development, Fall 2011 - ETSU iTunes
  13. CSCI 5710 e-Commerce Implementation, Fall 2015 - ETSU iTunes
  14. MOOC - Web Development - Udacity
  15. Web Technologies Prof. Dr. Christoph Meinel - HPI
  16. Full Stack Open - University of Helsinki (Modern Web Development with React, Node.js, GraphQL, TypeScript)

Theoretical CS and Programming Languages (68)

  1. MIT 18.404J Theory of Computation - Fall 2020 - Lecture Slides
  2. MIT 18.404J Theory of Computation - Fall 2020 - Video Lectures
  3. MOOC - Compilers - Stanford University
  4. CS 6120: Advanced Compilers: The Self-Guided Online Course - Cornell University
  5. CS 164 Hack your language, UC Berkeley: (Lectures - Youtube)
  6. Theory of computation - Shai Simonson
  7. CS 173 Programming Languages, Brown University: (Book)
  8. CS Theory Toolkit at CMU 2020
  9. CS 421 - Programming Languages and Compilers, UIUC
  10. CSC 253 - CPython internals: A ten-hour codewalk through the Python interpreter source code, University of Rochester
  11. CSE341 - Programming Languages, Dan Grossman, Spring 2013 - University of Washington
  12. CSEP 501 - Compiler Construction, University of Washington: (Lectures - Youtube)
  13. CSEP 505 Programming Languages, Winter 2015 - University of Washington
  14. DMFP - Discrete Mathematics and Functional Programming, Wheaton College
  15. CS 374 - Algorithms & Models of Computation (Fall 2014), UIUC: (Lecture videos)
  16. 6.045 Automata, Computability, and Complexity, MIT: (Lecture Videos)
  17. MOOC - Automata - Jeffrey Ullman - Coursera
  18. CS581 Theory of Computation - Portland State University: (Lectures - Youtube)
  19. Theory of Computation - Fall 2011 UC Davis
  20. TDA555 Introduction to Functional Programming - Chalmers University of Technology: (Lectures - YouTube)
  21. Ryan O'Donnell Theoretical Computer Science Talks
  22. Philip Wadler Haskell lecture recordings
  23. Functional Programming (2021) - University of Nottingham
  24. Functional Programming - University of Edinburgh - 2016-17
  25. MOOC - Functional Programming Principles in Scala by Martin Odersky
  26. CS294 - Program Synthesis for Everyone
  27. MOOC - Principles of Reactive Programming, Scala - Coursera
  28. Category Theory for Programmers, 2014 - Bartosz Milewski: (YouTube)
  29. 2012 Lectures
  30. 2013 Lectures
  31. 2014 Lectures
  32. 2015 Lectures
  33. 2016 Lectures
  34. Latest YT playlists
  35. Inf1 - Computation and Logic 2015 - University of Edinburgh
  36. Compiler Design - IISC Bangalore
  37. Compiler Design - IIT Kanpur
  38. Principles of Programming Languages - IIT Delhi
  39. Principles of Compiler Design - IISC Bangalore
  40. Functional Programming in Haskell - IIT Madras
  41. Theory of Computation - IIT Kanpur
  42. Theory of Automata, Formal Languages and Computation - IIT Madras
  43. Theory of Computation - IIT Kanpur
  44. Logic for CS - IIT Delhi
  45. Principles of Compiler Design - Swarthmore College
  46. Undergrad Complexity Theory at CMU
  47. Graduate Complexity Theory at CMU
  48. Great Ideas in Theoretical Computer Science at CMU
  49. Another link
  50. Analysis of Boolean Functions at CMU
  51. Theoretical Computer Science (Bridging Course)(Tutorial) - SS 2015
  52. Languages & Translators - UCLouvain LINFO2132
  53. Compiler Design by Sorav Bansal
  54. OCaml Programming: Correct + Efficient + Beautiful
  55. Columbia IEOR E4004 Introduction to Operations Research: Deterministic Models summer 2005, by Jay Sethuraman
  56. Columbia IEOR E4106 Introduction to Operations Research: Stochastic Models spring 2005, by Ward Whitt
  57. Columbia ELEN E6711 Stochastic Models in Information Systems fall 2005, by Yuliy Barsyhnikov
  58. Columbia ELEN E6717 Information Theory fall 2003, by Vittorio Castelli
  59. University of Washington EE514A/EE515A - Information Theory I/II fall 2013, by Jeff Bilmes
  60. CMU 15 150 Principles of Functional Programming summer 2023, by Brandon Wu
  61. CMU 21 738 Extremal Combinatorics spring 2020, by Po-Shen Loh
  62. JHU Domain-Specific Languages (DSL) Class (Summer 2018)
  63. Spring 2019 - Probabilistically Checkable and Interactive Proof Systems (Alessandro Chiesa)
  64. Algebraic Coding Theory - Stanford University
  65. CS60094 Computational Number Theory
  66. KAIST CS420: Compiler Design
  67. CS402 Introduction to Logic for Computer Science (Spring 2020, KAIST)
  68. CS520 Theories of Programming Languages (Fall 2020, KAIST)

Embedded Systems (25)

  1. EE319K Embedded Systems - UT Austin
  2. EE445L Embedded Systems Design Lab, Fall 2015, UTexas
  3. CS149 Introduction to Embedded Systems - Spring 2011 - UCBerkeley
  4. CSE/ECE 474 Introduction to Embedded Systems - University of Washington: (Lectures - YouTube-Spring 21)
  5. ECE 4760 Designing with Microcontrollers Fall 2016, Cornell University: (Lectures - Youtube)
  6. ECE 5760 - Advanced Microcontroller Design and system-on-chip, Spring 2016 - Cornell University
  7. Internet of Things by Prof. Dr.-Ing. Dietmar P. F. Möller
  8. 18 642 - Embedded Software Engineering, Fall 2021 - CMU
  9. CSE 351 - The Hardware/Software Interface, Spring 16 - University of Washington: (Coursera)
  10. ECE 5030 - Electronic Bioinstrumentation, Spring 2014 - Cornell University
  11. ECE/CS 5780/6780 - Embedded Systems Design, Spring 14 - University of Utah
  12. EECS 373 - Introduction to Embedded System Design - University of Michigan: (Lectures - YouTube-Fall 24) (Lectures - YouTube-Fall 23)
  13. Embedded Systems Class - Version 1 - 2011 - UNCC
  14. Embedded Systems using the Renesas RX63N Processor - Version 3 - UNCC
  15. Software Engineering for Embedded Systems (WS 2011/12) - HPI University of Potsdam
  16. Embedded Software Testing - IIT Madras
  17. Embedded Systems - IIT Delhi
  18. Embedded Systems Design - IIT Kharagpur
  19. ARM Based Development - IIT Madras
  20. Software Engineering for Self Adaptive Systems - iTunes - HPI University of Potsdam
  21. EE260 Embedded Systems by Robert Paz
  22. IoT Summer School
  23. ECSE 421 - Embedded Systems - McGill
  24. NOC:Advanced IOT Applications - IISc Bangalore
  25. NOC:Design for internet of things - IISc Bangalore

Real time system evaluation (14)

  1. Performance evaluation of Computer systems - IIT Madras
  2. Real Time systems - IIT Karaghpur
  3. EE 380 Colloquium on Computer Systems - Stanford University
  4. CSE 6220 / CX 4220 Introduction to High Performance Computing - Georgia Tech: Videos - Sp 2025 Website - Fa 2024 Videos - Fa 2024
  5. CSE 6230 High Performance Computing: Tools and Applications - Georgia Tech: Videos - Sp 2024
  6. System storages - IISc Bangalore
  7. High Performance Computing - IISC Bangalore
  8. 2023 High Performance Computing Course Prof Dr - Ing Morris Riedel: (2022)
  9. High Performance Computing | Udacity
  10. UCLA Stats 205 Hierarchical Linear Models spring 2024, by Jingyi Jessica Li
  11. Stanford AA 203 Optimal and Learning-Based Control, by Marco Pavone, Daniele Gammelli: (Videos - Sp 2026)
  12. FSU EML 4930 / 5930 Applied Optimal Control spring 2025, by Christian Hubicki: (Spring 2023)
  13. UF EML 6934 Optimal Control spring 2012, by Anil V. Rao
  14. Real-World Algorithms for IoT and Data Science - UIUC

Computer Organization and Architecture (51)

  1. How Computers Work - Aduni
  2. CS 61C - Machine Structures, UC Berkeley Spring 2015
  3. 6.004 - Computation Structures Spring 2013, MIT
  4. CS/ECE 3810 Computer Organization, Fall 2015, , University of Utah: (YouTube)
  5. Digital Computer Organization - IIT Kharagpur
  6. Computer Organization - IIT Madras
  7. CS-224 - Computer Organization, 2009-2010 Spring, Bilkent University: (YouTube playlist)
  8. INFORMATICS 2C - INTRODUCTION TO COMPUTER SYSTEMS (AUTUMN 2016) - University of Edinburgh
  9. 18-447 - Introduction to Computer Architecture, CMU: (Lectures - YouTube - Fall 15)
  10. CSEP 548 - Computer Architecture Autumn 2012 - University of Washington
  11. CS/ECE 6810 Computer Architecture, Spring 2016, University of Utah: (YouTube)
  12. MOOC - Computer Architecture, David Wentzlaff - Princeton University/Coursera
  13. Computer Architecture - ETH Zürich - Fall 2019
  14. Digital Circuits and Computer Architecture - ETH Zurich - Spring 2017
  15. Computer Architecture - IIT Delhi
  16. Computer Architecture - IIT Kanpur
  17. Computer Architecture - IIT Madras
  18. High Performance Computer Architecture - IIT Kharagpur
  19. BE5B35APO - Computer Architectures, Spring 2025, CTU - FEE: (RISC-V simulator - QtRvSim), (YouTube - Spring 2025), (YouTube - Spring 2022)
  20. BE4M35PAP - Advanced Computer Architectures, Winter 2025, CTU - FEE: (VHSky.cz - Winter 2025)
  21. CS773: Computer Architecture for Performance and Security - IIT Bombay
  22. COL718 - Architecture of High Performance Computers - IIT Delhi
  23. CS 695: Virtualization and Cloud Computing - IIT Bombay -Spring 2021
  24. CS 267 Applications of Parallel Computers, Spring 18 - UC Berkeley: (YouTube-Spring 18) (Notes-Spring 16) (YouTube-Spring 16)
  25. MOOC - Heterogeneous Parallel Programming - Coursera
  26. ECE 498AL - Programming Massively Parallel Processors
  27. ECE 1756 - Reconfigurable Computing and FPGA Architecture - University of Toronto - Fall 2020: (YouTube-Fall 2022)
  28. Parallel Computing - IIT Delhi
  29. Parallel Architectures 2012/13- University of Edinburgh
  30. ELEC2141 Digital Circuit Design, UNSW
  31. Digital Systems Design - IIT Kharagpur
  32. Digital Design Course - 2015 - UNCC
  33. CS1 - Higher Computing - Richard Buckland UNSW
  34. MOOC - From NAND to Tetris - Building a Modern Computer From First Principles: (YouTube)
  35. System Validation, TU Delft
  36. High Performance Computing - IISC Bangalore
  37. Introduction to ARM - Open SecurityTraining
  38. Intro x86 (32 bit) - Open SecurityTraining
  39. Intermediate x86 (32 bit) - Open SecurityTraining
  40. EECS 151 / 251A Introduction to Digital Design and Integrated Circuits - UC Berkeley - Fall 2025
  41. Design of Digital Circuits - ETH Zürich - Spring 2019
  42. Onur Mutlu @ TU Wien 2019 - Memory Systems
  43. Memory Systems Course - Technion, Summer 2018
  44. UC Berkeley EECS16A Designing Information Devices and Systems I summer 2020, by Grace Kuo, Panos Zarkos, Urmita Sikder
  45. UC Berkeley EECS 16B Designing Information Devices and Systems II fall 2020, by Seth Sanders, Miki Lustig
  46. EE 503 - Statistical Signal Processing and Modeling - Fall 2020 - METU: (YouTube)
  47. ELEN E4810 - DIGITAL SIGNAL PROCESSING - Fall 2013 - Columbia
  48. ELEN E4896 - MUSIC SIGNAL PROCESSING - Spring 2016 - Columbia
  49. Columbia ELEN E6820 Speech and Audio Processing spring 2006, by Dan Ellis
  50. CMU 11 751 / 18 781 Speech Recognition and Understanding fall 2023, by Shinji Watanabe: (Fall 2022)
  51. CMU 11 492 Speech Processing fall 2021, by Alan W. Black

Security (37)

  1. Internet Security (WT 2018/19) - HPI University of Potsdam
  2. 6.1600 Foundations of Computer Security - MIT Fall 2023
  3. 6.858 Computer Systems Security - MIT OCW Fall 2014
  4. 6.566 Computer Systems Security - MIT OCW Spring 2026: (Videos)
  5. CS 253 Web Security - Stanford University
  6. CS 161: Computer Security, UC Berkeley: (Videos - Fall 2023) (Videos - Fall 2025) (Spring 2025)
  7. 6.875 - Cryptography - Fall 2021 - MIT: (Spring 2018)
  8. CSEP590A - Practical Aspects of Modern Cryptography, Winter 2011 - University of Washington: (Videos)
  9. CS461/ECE422 - Computer Security - University of Illinois at Urbana-Champaign: (Videos)
  10. Introduction to Cryptography, Christof Paar - Ruhr University Bochum, Germany
  11. ECS235B Foundations of Computer and Information Security - UC Davis
  12. CIS 4930/ CIS 5930 - Offensive Computer Security, Florida State University
  13. Introduction to Information Security I - IIT Madras
  14. Information Security - II - IIT Madras
  15. Introduction to Cryptology - IIT Roorkee
  16. Cryptography and Network Security - IIT Kharagpur
  17. Internet Security - Weaknesses and Targets (WT 2015/16): (WT 2012/13 (YouTube))
  18. IT Security, Steven Gordon - Thammasat University, Thailand
  19. Security and Cryptography, Steven Gordon - Thammasat University, Thailand
  20. MOOC - Cryptography - Coursera
  21. MOOC - Intro to Information Security - Udacity
  22. ICS 444 - Computer & Network Security
  23. Privacy and Security in Online Social Networks - IIT Madras
  24. Malware Dynamic Analysis - Open SecurityTraining: (YouTube)
  25. CSN09112 - Network Security and Cryptography - Bill Buchanan - Edinburgh Napier
  26. CSN10107 - Security Testing and Network Forensics - Bill Buchanan - Edinburgh Napier
  27. CSN11123 - Advanced Cloud and Network Forensics - Bill Buchanan - Edinburgh Napier
  28. CSN11117 - e-Security - Bill Buchanan - Edinburgh Napier
  29. CSN08704 - Telecommunications - Bill Buchanan - Edinburgh Napier
  30. CSN11128 - Incident Response and Malware Analysis - Bill Buchanan - Edinburgh Napier
  31. Internet Security for Beginners by Dr. Christoph Meinel - HPI
  32. Offensive Security and Reverse Engineering, Chaplain University by Ali Hadi
  33. Computer Systems Security, Fall 2020, Vinod Ganapathy, IISc Bangalore
  34. UC Berkeley CS 161 Computer Security, Summer 2021, by Nicholas Ngai and Peyrin Kao
  35. UCSD CS291A Differential Privacy Fall 2021, by Yu-Xiang Wang: (Youtube)
  36. Zero Knowledge Proofs MOOC, UC Berkeley RDI Center on Decentralization & AI
  37. Multimedia Security, Fall 2017, FAU

Computer Graphics (22)

  1. ECS 175 - Computer Graphics, Fall 2009 - UC Davis
  2. 6.837 - Computer Graphics - Spring 2017 - MIT
  3. 6.838 - Shape Analysis - Spring 2017- MIT
  4. Introduction to Computer Graphics - IIT Delhi
  5. Computer Graphics - IIT Madras
  6. Computer Graphics 2012, Wolfgang Huerst, Utrecht University
  7. CS 5630/6630 - Visualization, Fall 2016, University of Utah: (Lectures - Youtube)
  8. Advanced Visualization UC Davis
  9. Computer Graphics Fall 2011, Barbara Hecker
  10. Ray Tracing for Global Illumination, UCDavis
  11. Rendering / Ray Tracing Course, SS 2015 - TU Wien
  12. Computational Geometry - IIT Delhi
  13. CS 468 - Differential Geometry for Computer Science - Stanford University: (Lecture videos)
  14. CMU 15-462/662: Computer Graphics
  15. UC Berkeley CS184/284A Computer Graphics and Imaging Spring 2022, by Ren Ng: (YouTube playlist)
  16. CS 5610/6610 - Interactive Computer Graphics, Cem Yuksel - University of Utah: (Lectures - YouTube)
  17. CS 15-458/858: Discrete Differential Geometry - Carnegie Mellon University - Spring 2021
  18. IN2124: Basic Mathematical Tools for Imaging and Visualization - TUM - Winter 2021
  19. KAIST CS580 Computer Graphics (Spring 2024)
  20. Discrete Differential Geometry - CMU 15-458/858
  21. Machine Learning For Inverse Graphics - MIT
  22. Geometric Structures - KAIST

Image Processing and Computer Vision (38)

  1. Digital Image Processing - IIT Kharagpur
  2. CS 543 - Computer Vision – Spring 2017: (Recordings)
  3. CAP 5415 - Computer Vision - University of Central Florida: (Video Lectures)
  4. EE637 - Digital Image Processing I - Purdue University: (Videos - Sp 2011,Videos - Sp 2007)
  5. EE641 - Digital Image Processing II - Purdue University: (Videos - Fa 2013,Videos - Fa 2020)
  6. Computer Vision I: Variational Methods - TU München: (YouTube)
  7. Computer Vision II: Multiple View Geometry (IN2228), SS 2016 - TU München: (YouTube)
  8. EENG 512/CSCI 512 - Computer Vision - Colorado School of Mines
  9. Computer Vision for Visual Effects - RPI: (YouTube)
  10. Introduction to Image Processing - RPI: (YouTube)
  11. CAP 6412 - Advanced Computer Vision - University of Central Florida: (Video lectures) (Spring 2018)
  12. Digital Signal Processing - RPI
  13. UC Berkeley EE 123 Digital Signal Processing fall 2003, by Avideh Zakhor
  14. UC Berkeley EE 225B Digital Image Processing spring 2006, by Avideh Zakhor
  15. Gatech ECE 2026 Introduction to Signal Processing spring 2025, by Aaron Lanterman: (YouTube)
  16. Gatech ECE 4270 Digital Signal Processing Fundamentals spring 2021, by David Anderson: (YouTube)
  17. Gatech ECE 6250 Advanced Digital Signal Processing summer 2020, by David Anderson: (YouTube)
  18. Gatech ECE 6271 Adaptive Signal Processing winter 2023, by David Anderson: (YouTube)
  19. Advanced Vision 2014 - University of Edinburgh
  20. Photogrammetry Course - 2015/16 - University of Bonn, Germany
  21. MOOC - Introduction to Computer Vision - Udacity
  22. ECSE-4540 - Intro to Digital Image Processing - Spring 2015 - RPI
  23. Machine Learning for Computer Vision - Winter 2017-2018 - UniHeidelberg
  24. High-Level Vision - CBCSL OSU
  25. Advanced Computer Vision - CBCSL OSU
  26. Introduction to Image Processing & Computer Vision - CBCSL OSU
  27. Machine Learning for Computer Vision - TU Munich
  28. Biometrics - IIT Kanpur
  29. Quantitative Big Imaging 2019 ETH Zurich
  30. Multiple View Geometry in Computer Vision
  31. Modern C++ Course For CV (2020) - University of Bonn
  32. Photogrammetry 1 Course – 2020 - University of Bonn
  33. Photogrammetry II Course 2020/21 - University of Bonn
  34. 3D Computer Vision - National University of Singapore
  35. Diagnostic Medical Image Processing - Fall 2014 - FAU: (Fall 2011) (Fall 2010) (Fall 2009)
  36. Interventional Medical Image Processing - Spring 2016 - FAU: (Spring 2015) (Spring 2012) (Spring 2011) (Spring 2009)
  37. Advances in Computer Vision - MIT
  38. Computer Vision Foundations Class (Summer 2020) - UniHeidelberg

Computational Physics (16)

  1. Statistics and Machine Learning for Astronomy
  2. Astronomical data analysis using Python 2021 - NRC IUCAA
  3. SPARC Workshop on Machine Learning in Solar Physics and Space Weather - CESSI IISER Kolkata
  4. Data-Driven Methods and Machine Learning in Atmospheric Sciences - IISC
  5. Computational Astrophysics - AstroTwinCoLo, 2015
  6. Astroinformatics 2019 Conference - Caltech
  7. Space Science with Python - Astroniz
  8. EE 210 Applied Electromagnetic Theory, UC Berkeley: Videos - Fa 2025
  9. Computational Physics Course in Python, Rutgers 2021
  10. Landau Computational Physics Course
  11. Statistical Methods and Machine Learning in High Energy Physics
  12. Physics Informed Machine Learning by Steve Brunton
  13. Physics-informed machine learning meets engineering seminar series
  14. Physics Informed Machine Learning Workshop
  15. Jake VanderPlas: Machine learning in Astronomy python tutorial
  16. Machine Learning and Physics - UniHeidelberg

Computational Biology (38)

  1. ECS 124 - Foundations of Algorithms for Bioinformatics - Dan Gusfield, UC Davis: (YouTube)
  2. CSE549 - Computational Biology - Steven Skiena - 2010 SBU
  3. 7.32 Systems Biology, Fall 2014 - MIT OCW
  4. 6.802J/ 6.874J Foundations of Computational and Systems Biology - MIT OCW
  5. 6.S897 Machine Learning For Healthcare
  6. 6.047/6.878 Machine Learning for Genomics Fall 2020 - MIT
  7. 6.874 MIT Deep Learning in Life Sciences - Spring 2021 - MIT
  8. 6.047/6.878 Public Lectures on Computational Biology: Genomes, Networks, Evolution - MIT
  9. Bio 84 - Your Genes and Your Health, Stanford University
  10. BioMedical Informatics 231 Computational Molecular Biology, Stanford University
  11. BioMedical Informatics 258 Genomics, Bioinformatics & Medicine, Stanford University
  12. 03-251: Introduction to Computational Molecular Biology - Carnegie Mellon University
  13. 03-712: Biological Modeling and Simulation - Carnegie Mellon University
  14. MOOC - Bioinformatics Algorithms: An Active Learning Approach - UC San Diego/Coursera
  15. Neural Networks and Biological Modeling - Lecturer: Prof. Wulfram Gerstner - EPFL
  16. Video Lectures of Wulfram Gerstner: Computational Neuroscience - EPFL
  17. An Introduction To Systems Biology
  18. Introduction to Bioinformatics, METUOpenCourseWare
  19. MOOC - Algorithms for DNA Sequencing, Coursera
  20. Frontiers of Biomedical Engineering with W. Mark Saltzman - Yale
  21. NOC:Computational Systems Biology - IIT Madras
  22. NOC:BioInformatics:Algorithms and Applications - IIT Madras
  23. MLCB24: Machine Learning in Computational Biology, Fall 2024 - Manolis Kellis - MIT
  24. Data Science and AI for Neuroscience Summer School - Caltech Neuroscience
  25. Theoretical and Computational Neuroscience Summer School - 2024 - CNeuro
  26. Neuroscience 299: Computing with High-Dimensional Vectors - Fall 2021 - UC Berkeley
  27. BIO410/510 Bioinformatics - California State University, Monterey Bay
  28. BIO412: Comparative Genomics - California State University, Monterey Bay
  29. CENG 465 - Introduction to Bioinformatics (Spring 2020-2021)
  30. UCLA Stats M254 Statistical Methods in Computational Biology spring 2024, by Jingyi Jessica Li
  31. Cell and Molecular Biology for Engineers ETH Zurich
  32. Statistical Models in Computational Biology
  33. UC Berkeley CS 198-96 Introduction to Neurotechnology fall 2020
  34. MLCB24 - Machine Learning in Computational Biology Fall 2024: (Fall 2018) (Fall 2019)
  35. Introduction to Neural Computation - MIT OCW
  36. Data Science for Biologists - Steve Brunton
  37. Big Data and Biological Networks IIT Madras
  38. Johns Hopkins Mathematical foundations of Biomedical Engineering, by Reza Shadmehr

Quantum Computing (29)

  1. 15-859BB: Quantum Computation and Quantum Information 2018 - CMU: (Youtube)
  2. Quantum Computation and Information at CMU
  3. Ph/CS 219A Quantum Computation - Prof Preskill - Caltech
  4. Quantum Mechanics and Quantum Computation - Umesh Vazirani
  5. Introduction to quantum computing course 2022 - NYU
  6. Phys 1470 - Foundations of Quantum Computing and Quantum Information - U of Pittsburgh
  7. Introduction to Quantum Computing From a Layperson to a Programmer in 30 Steps (EE225 SJSU)
  8. Quantum Computing Hardware and Architecture (EE274 SJSU)
  9. Quantum Physics for Non-Physicists 2021 - ETH Zurich: (2020)
  10. Introduction to Quantum Computing and Quantum Hardware - Qiskit
  11. Understanding Quantum Information and Computation - Qiskit
  12. Lectures in Quantum Computation and Quantum Information (IIT Madras)
  13. Quantum Information and Computing by Prof. D.K. Ghosh
  14. Quantum Computing by Prof. Debabrata Goswami
  15. The Building Blocks of a Quantum Computer: Part 1 - TU Delft
  16. The Building Blocks of a Quantum Computer: Part 2 - TU Delft
  17. Quantum Cryptography - TU Delft
  18. Introduction to Quantum Information
  19. Quantum Computing for Everyone -- Part 1: (Part 2)
  20. Quantum Computer Systems – UChicago
  21. Quantum computing for the determined - Michael Nielsen
  22. Quantum Computing
  23. Advanced Topics in Quantum Info Th.
  24. Theory of Quantum Communication - University of Waterloo - Fall 2020
  25. Intro to Quantum Computing - Nathan Wiebe
  26. COMS 4281 - Introduction to Quantum Computing -Columbia University
  27. Introduction to Quantum Information Science: (Online book, PDF)
  28. A practical introduction to Quantum Computing: from Qubits to Quantum Machine Learning: CERN
  29. PSI 2018/2019 - Quantum Information Review (Gottesman)

Robotics and Control (92)

  1. ROB 101: Computational Linear Algebra - University of Michigan: (Youtube - Fall 2021)
  2. ROB 102: Introduction to AI and Programming - University of Michigan
  3. Robotics 201: Calculus for the Modern Engineer - University of Michigan
  4. ROB 311: How to Build Robots and Make Them Move - University of Michigan
  5. ROB 320: Robot Operating Systems - University of Michigan
  6. ROB 501: Mathematics for Robotics - University of Michigan: (Youtube)
  7. ROB 530 MOBILE ROBOTICS at U of Michigan - WINTER 2022 -- Instructor: Maani Ghaffari
  8. Autorob Winter 2022 - University of Michigan
  9. DeepRob Winter 2023 - University of Michigan
  10. CS 223A - Introduction to Robotics, Stanford University
  11. 6.832 Underactuated Robotics - MIT OCW
  12. 6.801 Machine Vision, MIT Fall 2020 - Berthold Horn: (YouTube)
  13. CS287 Advanced Robotics at UC Berkeley Fall 2019 -- Instructor: Pieter Abbeel
  14. CS 287 - Advanced Robotics, Fall 2011, UC Berkeley: (Videos)
  15. CMU 16-715 Robot Dynamics 2022 - CMU
  16. CMU 16-745 Optimal Control 2024 - CMU: (Lecture notebooks) (YouTube-2023) (YouTube-2022)
  17. CMU 16-745 Optimal Control Recitations 2024 - CMU: (YouTube-2023)
  18. CE 356 Elements of Hydraulic Engineering Spring 2025 - UT Austin
  19. CE 397 Control Theory for Smart Infrastructure Spring 2023 - UT Austin
  20. CS235 - Applied Robot Design for Non-Robot-Designers - Stanford University
  21. Lecture: Visual Navigation for Flying Robots: (YouTube)
  22. CS 205A: Mathematical Methods for Robotics, Vision, and Graphics (Fall 2013)
  23. Optimization and Learning for Robot Control - University of Trento, Italy: (YouTube-2025)
  24. Robotics 1, Prof. De Luca, Università di Roma: (YouTube)
  25. Robotics 2, Prof. De Luca, Università di Roma: (YouTube)
  26. Robot Mechanics and Control, SNU
  27. Introduction to Robotics Course - UNCC
  28. SLAM Lectures
  29. CSE 478 – Autonomous Robotics – Winter 2025 - University of Washington: (Winter 2024)
  30. CSE 571 – AI-Robotics – Spring 2023 - University of Washington
  31. EE 259 – Principles of Sensing for Autonomy – Spring 2023 - Stanford University
  32. ME 597 – Autonomous Mobile Robotics – Fall 2014
  33. ME 780 – Perception For Autonomous Driving – Spring 2017
  34. ME780 – Nonlinear State Estimation for Robotics and Computer Vision – Spring 2017
  35. METR 4202/7202 -- Robotics & Automation - University of Queensland
  36. UIUC CS-588 Autonomous Vehicle System Engineering Outline
  37. Robotics - IIT Bombay
  38. Introduction to Machine Vision
  39. 6.834J Cognitive Robotics - MIT OCW
  40. Hello (Real) World with ROS – Robot Operating System - TU Delft
  41. Programming for Robotics (ROS) - ETH Zurich
  42. Mechatronic System Design - TU Delft
  43. CS 206 Evolutionary Robotics Course Spring 2020
  44. Foundations of Robotics - UTEC 2018-I
  45. Robotics and Control: Theory and Practice IIT Roorkee
  46. Mechatronics
  47. ME142 - Mechatronics Spring 2020 - UC Merced
  48. Mobile Sensing and Robotics - Bonn University
  49. MSR2 - Sensors and State Estimation Course (2020) - Bonn University
  50. SLAM Course (2013) - Bonn University
  51. ENGR486 Robot Modeling and Control (2014W)
  52. Robotics by Prof. D K Pratihar - IIT Kharagpur
  53. Introduction to Mobile Robotics - SS 2019 - Universität Freiburg
  54. Robot Mapping - WS 2018/19 - Universität Freiburg
  55. Mechanism and Robot Kinematics - IIT Kharagpur
  56. Self-Driving Cars - Cyrill Stachniss - Winter 2020/21 - University of Bonn)
  57. Aerial Robotics - University of Pennsylvania (UPenn)
  58. Modern Robotics - Northwestern University
  59. MIT 6.4210/6.4212 - Robotic Manipulation - MIT: (Youtube)
  60. Industrial Robotics and Automation - IIT (ISM) Dhanbad
  61. MEE5114 Advanced Control for Robotics from Southern University of Science and Technology
  62. Self-Driving Cars, Andreas Geiger
  63. Signal Processing: An Introduction by Nathan Kutz
  64. UC Santa Barbara ME 269 Network Systems, Dynamics and Control fall 2021, by Francesco Bullo
  65. Cornell MAE 4710/5710 Applied Dynamics spring 2020, by Andy Ruina: (Part 2)
  66. Cornell MAE 4730/5730 Intermediate Dynamics fall 2020, by Andy Ruina
  67. CMU 16 299 Introduction to Feedback Control Systems spring 2022, by Chris Atkeson
  68. University of Wisconsin-Madison ECE 332 Feedback Control Systems fall 2021, by Steven Fredette
  69. MAE 509 Linear Matrix Inequality Methods in Optimal and Robust Control, by Matthew M. Peet
  70. UCCS ECE4590/ECE5590 Model Predictive Control, by M. Scott Trimboli
  71. EPFL ME 425 Model Predictive Control fall 2020, by Colin Jones
  72. Robots That Learn - UC Berkeley CS 294-277
  73. Data-Driven Dynamical Systems with Machine Learning - Steve Brunton
  74. Data-Driven Control with Machine Learning - Steve Brunton
  75. Wheeled Mobile Robots
  76. Introduction to Mobile Robots and Robot Operating System (ROS), HSE 2021
  77. Surgical Robotics Lectures - Carleton University
  78. Introduction to Mechatronic and Robotics - IIT Bombay
  79. Robotics Fall 2020 - UIC
  80. Introduction to Robotics @ Princeton: (Slides)
  81. Evolutionary robotics course. Spring 2025
  82. Robotics: Basics and Selected Advanced - IISC Bangalore
  83. Nonlinear Control Design Jan 2024 - IIT Bombay
  84. Intelligent Control of Robotic Systems - IIT Roorkee
  85. Mobile Robot Systems Course - Amanda Prorok, University of Cambridge
  86. Robot Academy - Peter Corke
  87. ENAE 788M: Hands On Autonomous Aerial Robotics - University of Maryland
  88. Robot Learning 2025: Foundational Models for Robotics and Scaling DeepRL - Montreal Robotics
  89. Mila Robot Learning Seminar - Montreal Robotics
  90. Johns Hopkins Learning, Estimation, and Control spring 2026, by Reza Shadmehr: Youtube
  91. SUSTech ME424 Modern Control and Estimation ( Kalman Filter Theory ) fall 2021, by Wei Zhang: Youtube 1Youtube 2
  92. Robot Learning - ETH Zurich: (Youtube, Guest lectures)

Computational Finance (18)

  1. COMP510 - Computational Finance - Steven Skiena - 2007 HKUST
  2. Computational Finance Course - Prof Grzelak
  3. Financial Engineering Course: Interest Rates and xVA - Prof Grzelak
  4. MOOC - Mathematical Methods for Quantitative Finance, University of Washington/Coursera)
  5. 18.S096 Topics in Mathematics with Applications in Finance, Fall 2013 MIT OCW
  6. 18.642 Topics in Mathematics with Applications in Finance, Fall 2024 MIT OCW
  7. Computational Finance - Universität Leipzig
  8. Machine Learning for Trading | Udacity
  9. ACT 460 / STA 2502 – Stochastic Methods for Actuarial Science - University of Toronto
  10. STA 4505H – High Frequency & Algorithmic trading - University of Toronto
  11. Mathematical Finance - IIT Guwahati
  12. Quantitative Finance - IIT Kanpur
  13. Financial Derivatives & Risk Management - IIT Roorkee
  14. Financial Mathematics - IIT Roorkee
  15. Harvard Economics 2355 Deep Learning for Economics spring 2023, by Melissa Dell
  16. UW ECON 484 Econometrics and Data Science spring 2020, by Gregory Duncan
  17. MATH69122 Stochastic Control for Finance
  18. UC Davis MAT 133 Mathematical Finance Spring 2024, by Matthias Köppe: (Spring 2021)

Network Science (5)

  1. Network Science, 2021 - HSE
  2. Network Science TU Graz
  3. MATH/COMP 479 Network Science Macalester College
  4. ACM Winter School on Network Science _Dec 2023, Ahmedabad University
  5. Network Science 2021/2022 (ENS Lyon)

Blockchain Development (12)

  1. Blockchain, Solidity, and Full Stack Web3 Development with JavaScript
  2. Blockchain Fundamentals Decal 2018 - Berkeley DeCal
  3. Blockchain for Developers Decal - Spring 2018 - Berkeley DeCal
  4. Cryptocurrency Engineering and Design - Spring 2018 - MIT
  5. 15.S12 Blockchain and Money, Fall 2018 - MIT
  6. Bitcoin and Cryptocurrency Technologies - Arvind Narayanan, Princeton University: (YouTube Lectures) (Free book PDF)
  7. Blockchain - Foundations and Use Cases
  8. Solidity for Beginners - Dapp University
  9. Master Solidity - Dapp University
  10. IPFS Inter Planetary File System Dapp University
  11. Solidity, Blockchain, and Smart Contract Course – Beginner to Expert Python Tutorial - FreeCodingCamp
  12. Web 3.0 - Build Realtime Decentralized applications

Misc (55)

  1. CS147 - Introduction to Human-Computer Interaction Design - Stanford
  2. CSEP 510 - Human Computer Interaction
  3. Programming for Designers - COMP1400-T2 (2010) - UNSW
  4. CS50's Introduction to Game Development
  5. MIT CMS.611J Creating Video Games, Fall 2014
  6. MOOC - Beginning Game Programming with C# - Coursera
  7. Gatech ECE4795 GPU Programming for Video Games, Summer 2021
  8. COMP 4300 - C++ Game Programming, Memorial University, Fall 2024 - David Churchill
  9. COMP 4303 - AI for Video Games, Memorial University, Winter 2023 - David Churchill
  10. Introduction to Spatial Data Science, Autumn 2016, University of Chicago
  11. Spatial Regression Analysis, Spring 2017, University of Chicago
  12. Spatial Data Science, Autumn 2017, University of Chicago
  13. Introduction to Geographic Information Systems - IIT Roorkee
  14. MOOC - Matlab - Coursera
  15. Computing for Computer Scientists - University of Michigan
  16. Linux System Administration Decal, Spring 2025, UC Berkeley
  17. Linux Implementation/Administration Practicum - Redhat by Tulio Llosa
  18. Innovative Computing - Harvard University
  19. Linux Programming & Scripting - IIT Madras
  20. Model Checking - IIT Madras
  21. Virtual Reality - IIT Madras
  22. Business Process Compliance (WT 2013/14) - HPI University of Potsdam
  23. Design Thinking for Digital Engineering (SS 2018) - Dr. Julia von Thienen - HPI
  24. CS224w – Social Network Analysis – Autumn 2017 - Stanford University
  25. The Missing Semester of Your CS Education
  26. University of Crete, Computer Science video lectures (mostly Greek language lectures, very few 100% English-speaking courses). Very popular CS destination for European Erasmus students
  27. Stanford EE274 I Data Compression: Theory and Applications I 2023
  28. Probabilistic Methods - University of Waterloo
  29. Free Probability Theory and Ramanujan Graphs - Spring 2024
  30. Asymptotics and perturbation methods - Prof. Steven Strogatz
  31. ETH Zürich AI in the Sciences and Engineering
  32. Introduction to GIS Programming (Fall 2024) - Open Geospatial Solutions
  33. Gatech Guitar Amplification and Effects, by Aaron Lanterman
  34. Gatech ECE3084 Signals and Systems summer 2020, by Aaron Lanterman
  35. Gatech ECE4450 Analog Circuits for Music Synthesis spring 2021, by Aaron Lanterman
  36. UC Berkeley EE 120 Signals and Systems spring 2019, by Murat Arcak
  37. Stanford EE 102 spring 1999, Introduction to Signals and Systems, by Stephen Boyd
  38. Stanford EE 376a winter 2011, Information Theory, by Thomas Cover
  39. MIT RES.6.007 Signals and Systems, 1987 - MIT
  40. MIT 9.19 Computational Psycholinguistics, 2023 - MIT: (YouTube)
  41. MIT 21M.383 Computational Music Theory and Analysis, Spring 2023 - MIT: (YouTube)
  42. UCCS ECE4510/ECE5510 Feedback Control Systems, by Gregory Plett
  43. UCCS ECE4520/ECE5520 Multivariable Control Systems I, by Gregory Plett
  44. UCCS ECE4530/ECE5530 Multivariable Control Systems II, by Gregory Plett
  45. UCCS ECE4540/ECE5540 Digital Control Systems, by Gregory Plett
  46. UCCS ECE4550/ECE5550 Applied Kalman Filtering, by Gregory Plett
  47. UCCS ECE4560/ECE5560 System Identification, by Gregory Plett
  48. UCCS ECE4570/ECE5570 Optimization for Systems and Control, by M. Scott Trimboli
  49. UCCS ECE4580/ECE5580 Multivariable Control in the Frequency Domain, by M. Scott Trimboli
  50. UCCS ECE4710/ECE5710 Modeling, Simulation, and Identification of Battery Dynamics, by Gregory Plett
  51. UCCS ECE4720/ECE5720 Battery Management and Control, by Gregory Plett
  52. Purdue ME 597 Distributed Energy Resources Spring 2024, by Kevin J. Kircher
  53. Stanford AA228V/CS238V Validation of Safety Critical Systems Winter 2025, by Sydney Michelle Katz
  54. RES.1-002 - Introduction to R and Geographic Information Systems (GIS) - MIT - Fall 2023
  55. CMU 17 803 Empirical Methods spring 2026, by Bogdan Vasilescu: Youtube

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