Computer Science courses with video lectures

Introduction

• Please check NOTES for general information about this list.
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Table of Contents

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• Introduction to Computer Science
• Data Structures and Algorithms
• Systems Programming
  • Operating Systems
  • Distributed Systems
  • Real-Time Systems
• Database Systems
• Software Engineering
  • Object Oriented Design
  • Software Engineering
  • Software Architecture
  • Concurrency
  • Mobile Application Development
• Artificial Intelligence
• Machine Learning
  • Introduction to Machine Learning
  • Data Mining
  • Probabilistic Graphical Modeling
  • Deep Learning
  • Reinforcement Learning
  • Advanced Machine Learning
  • Natural Language Processing
  • Generative AI and LLMs
  • Computer Vision
  • Time Series Analysis
  • Optimization
  • Unsupervised Learning
  • Misc Machine Learning Topics
• Computer Networks
• Math for Computer Scientist
• Web Programming and Internet Technologies
• Theoretical CS and Programming Languages
• Embedded Systems
• Real time system evaluation
• Computer Organization and Architecture
• Security
• Computer Graphics
• Image Processing and Computer Vision
• Computational Physics
• Computational Biology
• Quantum Computing
• Robotics and Control
• Computational Finance
• Network Science
• Blockchain Development
• Misc

Courses

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Introduction to Computer Science

• COMP6991 - Rust Programming - UNSW (2022) | Video Playlist
• COMP1511 - Programming Fundamentals - UNSW (Complete Course: Lectures, Labs, Assignments, Exercises, Exams) | Video Playlist
• CS 10 - The Beauty and Joy of Computing - Spring 2015 - Dan Garcia - UC Berkeley InfoCoBuild
• 6.0001 - Introduction to Computer Science and Programming in Python - MIT OCW
• 6.001 - Structure and Interpretation of Computer Programs, MIT
• Introduction to Computational Thinking - MIT
• CS 50 - Introduction to Computer Science, Harvard University (cs50.tv)
• CS50R - Introduction to Programming with R (Lecture Videos)
• CS50P - CS50's Introduction to Programming with Python - Harvard (David J. Malan) (Lecture Videos)
• CS 61A - Structure and Interpretation of Computer Programs [Python], UC Berkeley
• CPSC 110 - Systematic Program Design [Racket], University of British Columbia
• CS50's Understanding Technology
• CSE 142 Computer Programming I (Java Programming), Spring 2016 - University of Washington
• CS 1301 Intro to computing - Gatech
• CS 106A - Programming Methodology, Stanford University (Lecture Videos)
• CS 106B - Programming Abstractions, Stanford University
• CS 106L - Standard C++ Programming(Lecture Videos)
• CS 106X - Programming Abstractions in C++
• CS 107 - Programming Paradigms, Stanford University
• CmSc 150 - Introduction to Programming with Arcade Games, Simpson College
• IN2377 - Concepts of C++ programming (Winter 2023), TUM (Winter 2022) (Summer 2022) (Summer 2021)
• IN1503 - Advanced C++ Programming, TUM
• LINFO 1104 - Paradigms of computer programming, Peter Van Roy, Université catholique de Louvain, Belgium - EdX
• FP 101x - Introduction to Functional Programming, TU Delft
• Introduction to Problem Solving and Programming - IIT Kanpur
• Introduction to programming in C - IIT Kanpur
• Programming in C++ - IIT Kharagpur
• Python Boot Camp Fall 2016 - Berkeley Institute for Data Science (BIDS)
• CS 101 - Introduction to Computer Science - Udacity
• 6.00SC - Introduction to Computer Science and Programming (Spring 2011) - MIT OCW
• 6.00 - Introduction to Computer Science and Programming (Fall 2008) - MIT OCW
• 6.01SC - Introduction to Electrical Engineering and Computer Science I - MIT OCW
• Modern C++ Course (2018) - Bonn University
• Modern C++ (Lecture & Tutorials, 2020, Vizzo & Stachniss) - University of Bonn
• UW Madison CS 368 C++ for Java Programmers Fall 2020, by Michael Doescher
• UW Madison CS 354 Machine Organization and Programming spring 2020, 2021, by Michael Doescher
• Cornell CS 1110 Introduction to Computing using Python fall 2020, by Walker White (Lecture Videos)
• Cornell ECE 4960 Computational and Software Engineering spring 2017, by Edwin Kan

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Data Structures and Algorithms

• ECS 36C - Data Structures and Algorithms (C++) - Spring 2020 - Joël Porquet-Lupine - UC Davis
• Programming and Data Structures with Python, 2021-2022, Sem I - by Prof. Madhavan Mukund, CMI
• Graph Algorithms - Robert Sedgewick - Princeton University
• EECS 477 - Introduction to Algorithms, Winter 2023, UMichigan
• EECS 498 / 598 - Advanced Graph Algorithms: Graph Algorithms via Graph Decomposition, Fall 2025, UMichigan
• EECS 498 / 598 - Advanced Graph Algorithms: Expanders and Fast Graph Algorithms, Fall 2021, UMichigan
• 6.006 - Introduction to Algorithms, MIT OCW
• MIT 6.006 Introduction to Algorithms, Spring 2020
• Algorithms: Design and Analysis 1 - Stanford University
• Algorithms: Design and Analysis 2 - Stanford University
• COS 226 Algorithms, Youtube, Princeton - by Robert Sedgewick and Kevin Wayne
• CSE 331 Introduction to Algorithm Design and Analysis, SUNY University at Buffalo, NY - Fall 2017 (Lectures) (Homework Walkthroughs)
• CSE 373 - Analysis of Algorithms, Stony Brook - Prof Skiena
• COP 3530 Data Structures and Algorithms, Prof Sahni, UFL (Videos)
• CS225 - Data Structures - University of Illinois at Urbana-Champaign(Video lectures)
• CS2 - Data Structures and Algorithms Sim-Mautner - UNSW
• Data Structures - Pepperdine University
• CS 161 - Design and Analysis of Algorithms, Prof. Tim Roughgarden, Stanford University
• 6.046J - Introduction to Algorithms - Fall 2005, MIT OCW
• Introduction to Algorithms (Spring 2020), MIT OCW
• 6.046 - Design and Analysis of Algorithms, Spring 2015 - MIT OCW
• CS 473 - Algorithms - University of Illinois at Urbana-Champaign (Notes - Jeff Erickson) (YouTube)
• COMP300E - Programming Challenges, Prof Skiena, Hong Kong University of Science and Technology - 2009
• 16s-4102 - Algorithms, University of Virginia (Youtube)
• CS 61B - Data Structures (Java) - UC Berkeley(Discussion 2022)
• CS 170 Algorithms - UCBerkeley Fall 2019, Youtube Fall 2018, Youtube Fall 2018,Bilibili 2013 Bilibili
• CS 159 Data-Driven Algorithm Design - Caltech Spring 2020, Youtube
• ECS 122A - Algorithm Design and Analysis, UC Davis
• CSE 373 - Data Structures and Algorithms, Winter 2024 - University of Washington (Winter 2024, Youtube) (Spring 2023, Notes) (Spring 2023, Youtube)
• CSEP 521 - Applied Algorithms, Winter 2013 - University of Washington (Videos)
• Data Structures And Algorithms - IIT Delhi
• Design and Analysis of Algorithms - IIT Bombay
• Programming, Data Structures and Algorithms - IIT Madras
• Design and Analysis of Algorithms - IIT Madras
• Fundamental Algorithms:Design and Analysis - IIT Kharagpur
• Programming and Data Structure - IIT Kharagpur
• Programming, Data structures and Algorithms - IIT Madras
• Programming, Data Structures and Algorithms in Python - IIT Madras
• Programming and Data structures (PDS) - IIT Madras
• COP 5536 Advanced Data Structures, Prof Sahni - UFL (Videos)
• CS 261 - A Second Course in Algorithms, Stanford University (Youtube)
• CS 224 - Advanced Algorithms, Harvard University (Lecture Videos) (Youtube)
• CS 6150 - Advanced Algorithms (Fall 2016), University of Utah
• CS 6150 - Advanced Algorithms (Fall 2017), University of Utah
• ECS 222A - Graduate Level Algorithm Design and Analysis, UC Davis
• 6.851 - Advanced Data Structures, MIT (MIT OCW)
• 6.854 - Advanced Algorithms, MIT (Prof. Karger lectures)
• CS264 Beyond Worst-Case Analysis, Fall 2014 - Tim Roughgarden Lecture (Youtube)
• CS364A Algorithmic Game Theory, Fall 2013 - Tim Roughgarden Lectures
• CS364B Advanced Mechanism Design, Winter 2014 - Tim Roughgarden Lectures
• CS3510 Algorithms, Spring 2024 - Georgia Tech Videos - Sp 2024 Videos - Su 2025
• CS4510 Automata and Complexity - Georgia Tech Videos - Su 2021 Videos - Su 2023 Videos - Su 2024 Videos - Su 2025
• Algorithms - Aduni
• 6.889 - Algorithms for Planar Graphs and Beyond (Fall 2011) MIT
• 6.890 Algorithmic Lower Bounds: Fun with Hardness Proofs - MIT OCW
• Computer Algorithms - 2 - IIT Kanpur
• Parallel Algorithm - IIT Kanpur
• Graph Theory - IISC Bangalore
• Data Structures - mycodeschool
• Algorithmic Game Theory, Winter 2020/21 - Uni Bonn
• NETS 4120: Algorithmic Game Theory, Spring 2023 - UPenn
• Introduction to Game Theory and Mechanism Design - IIT Kanpur
• 15-850 Advanced Algorithms - CMU Spring 2023
• CS 270. Combinatorial Algorithms and Data Structures, Spring 2021 (Youtube)
• CMU 15 850 Advanced Algorithms spring 2023, by Anupam Gupta
• UC Berkeley CS 294-165 Sketching Algorithms fall 2020, by Jelani Nelson (Youtube)
• UIUC CS 498 ABD / CS 598 CSC Algorithms for Big Data fall 2020, by Chandra Chekuri
• Algorithms for Data Science spring 2021, by Anil Maheshwari
• CMU 15 859 Algorithms for Big Data fall 2020, by David Woodruff
• CO 642 Graph Theory - University of Waterloo
• COMS W4241 Numerical Algorithms spring 2006, by Henryk Wozniakowski - Columbia
• Bonn Algorithms and Uncertainty summer 2021, by Thomas Kesselheim
• Harvard Information Theory 2022, by Gregory Falkovich
• Math 510 - Linear Programming and Network Flows - Colorado State University
• LINFO 2266 Advanced Algorithms for Optimization 2021, by Pierre Schaus - UCLouvain
• MIT 6.5210 / 6.854 / 18.415 Advanced Algorithms Fall 2013, 2020, 2021, 2022, by David Karger (Spring 2016, by Ankur Moitra)
• CMU 10 801 Advanced Optimization and Randomized Algorithms spring 2014, by Suvrit Sra and Alex Smola
• Purdue CS 381 Fundamental Algorithms, by Kent Quanrud (Spring 2022)
• Purdue CS 390 ATA Fundamental Algorithms Advanced, by Kent Quanrud (Spring 2025)
• Purdue CS 580 Graduate Algorithms, by Kent Quanrud (Spring 2023) (Spring 2024)
• Purdue CS 588 Randomized Algorithms, by Kent Quanrud (Fall 2022) (Spring 2024)
• UC Santa Cruz CSE 101 Intro to Data Structures and Algorithms fall 2022, by Seshadhri Comandur (Fall 2020)
• UC Santa Cruz CSE 201 Analysis of Algorithms winter 2022, by Seshadhri Comandur
• UC Santa Cruz CSE 202 Combinatorial Algorithms spring 2021, by Seshadhri Comandur
• UC Santa Cruz CSE 104, 204 Computational Complexity spring 2022, by Seshadhri Comandur (Fall 2020)
• UC Santa Cruz CSE 290A Randomized Algorithms spring 2020, by Seshadhri Comandur
• University of Maryland CMSC351 Introduction to Algorithms, by Mohammad Hajiaghayi
• University of Maryland CMSC858F Network Algorithms and Approximations, by Mohammad Hajiaghayi (YouTube playlists)
• University of Maryland CMSC858M Algorithmic Lower Bounds: Fun with Hardness Proofs, by Mohammad Hajiaghayi (YouTube playlists)
• University of Maryland UMD DATA602 / MSML602 Principles of Data Science spring 2024, by Mohammad Hajiaghayi
• Algorithms for Big-Data (Fall 2020) - Saket Saurabh
• CS498ABD - Algorithms for Big Data - UIUC, Fall 2020
• Advanced Data Structures
• CS60025 Algorithmic Game Theory - IIT KGP - Winter 2020
• CS60083 Parameterized Algorithms - IIT KGP
• Parameterized Complexity
• Structural Graph Theory - IIT Madras
• Information Theory - IISC Bangalore
• 6.5220/6.856J/18.416J Randomized Algorithms (Spring 2025) (Youtube)
• 18.225 - Graph Theory and Additive Combinatorics - MIT - Fall 2023
• Extremal graph theory at KAIST
• Advanced graph theory, KAIST 2020

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Systems Programming

• COMP1521 - Computer Systems Fundamentals (2025) - UNSW (Full Course: Website + Lectures, Labs, Assignments, Exercises, Exams, Video Playlist) | Watch Lectures
• 15-213 Introduction to Computer Systems, Fall 2015 - CMU
• Computer Systems: A programmer's Perspective
• CS361 - COMPUTER SYSTEMS - UIC
• CS 3650 - Computer Systems - Fall 2020 - Nat Tuck - NEU (Lectures - YouTube)
• CS 4400 – Computer Systems Fall 2016 - UoUtah
• Systems - Aduni
• CS110: Principles of Computer Systems - Stanford

• Operating Systems

  • Gatech CS6200 Intro to OS
  • APS 105 - Computer Fundamentals - Winter 2025 - Jon Eyolfson - University of Toronto (Winter 2024)
  • ECS 150 - Operating Systems and Systems Programming - Fall 2020 - Joël Porquet-Lupine - UC Davis
  • ECE 344 - Operating Systems - Fall 2024 - Jon Eyolfson - University of Toronto (Fall 2024 Section 2) (Fall 2023) (Fall 2022)
  • ECE 353 - Systems Software - Winter 2025 - Jon Eyolfson - University of Toronto (Winter 2024) (Winter 2023)
  • ECE 454 - Computer Systems Programming - Fall 2024 - Jon Eyolfson - University of Toronto
  • CS124 Operating Systems - California Institute of Technology, Fall 2018 - Youtube
  • CS 162 Operating Systems and Systems Programming, Spring 2015 - University of California, Berkeley (Fall 2020 - YouTube)
  • CS 4414 - Operating Systems, University of Virginia (rust-class)
  • CS 4414 Operating Systems, Fall 2018 - University of Virginia
  • CSE 421/521 - Introduction to Operating Systems, SUNY University at Buffalo, NY - Spring 2016 (Lectures - YouTube) (Recitations 2016) (Assignment walkthroughs)
  • CS 377 - Operating Systems, Fall 16 - Umass OS
  • CS 577 - Operating Systems, Spring 20 - Umass OS
  • CS 537 - Introduction to Operating Systems - Andrea & Remzi Arpaci-Dusseau, University of Wisconsin-Madison (OSTEP free textbook) (Lecture Videos)
  • 6.828 - Operating System Engineering [Fall 2014]
  • 6.S081 - Operating System Engineering [Fall 2020]
  • CSE 30341 - Operating Systems, Spr 2008
  • CSEP 551 Operating Systems Autumn 2014 - University of Washington
  • Introduction to Operating Systems - IIT Madras
  • CS194 Advanced Operating Systems Structures and Implementation, Spring 2013 InfoCoBuild, UC Berkeley
  • CSE 60641 - Graduate Operating Systems, Fall 08
  • Advanced Programming in the UNIX Environment
  • Operating System - IIT Madras
  • CS 153 Frontier Systems - Stanford University - Spring 2026
  • EE415: Introduction to Operating Systems (KAIST)

• Distributed Systems

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

• Real-Time Systems

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

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Database Systems

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

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Software Engineering

• Object Oriented Design

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

• Software Engineering

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

• Software Architecture

  • CS 411 - Software Architecture Design - Bilkent University
  • MOOC - Software Architecture & Design - Udacity
  • CS-310 Scalable Software Architectures

• Concurrency

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

• Mobile Application Development

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

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Artificial Intelligence

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

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Machine Learning

• Introduction to Machine Learning

  • Introduction to Machine Learning for Coders
  • MOOC - Statistical Learning, Stanford University
  • Statistical Learning with Python - Stanford Online
  • Foundations of Machine Learning Boot Camp, Berkeley Simons Institute
  • 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)
  • CS 156 - Learning from Data, Caltech
  • 10-601 - Introduction to Machine Learning (MS) - Tom Mitchell - 2015, CMU (YouTube)
  • 10-601 Machine Learning | CMU | Fall 2017
  • 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 - 301/601 - Introduction to Machine Learning - Fall 2023 - CMU
  • 6.036 - Machine Learning, Broderick - MIT Fall 2020
  • Mediterranean Machine Learning summer school 2024 (YouTube-2023) (YouTube-2022) (YouTube-2021)
  • LxMLS Lisbon Machine Learning School 2024 (YouTube-2023) (YouTube-2022) (YouTube-2021) (YouTube-2020)
  • Applied Machine Learning (Cornell Tech CS 5787, Fall 2020)
  • Stanford CS229: Machine Learning Course | Summer 2019 (Anand Avati) (Spring 2022)
  • CMS 165 Foundations of Machine Learning - 2019 - Caltech (Youtube)
  • CMS 165 Foundations of Machine Learning and Statistical Inference - 2020 - Caltech
  • Microsoft Research - Machine Learning Course
  • CS 446 - Machine Learning, Fall 2016, UIUC
  • CS 582 - Machine Learning for Bioinformatics, Fall 2024, UIUC
  • CMPUT 267 Machine Learning - Fall 2024 - University of Alberta (Youtube)
  • ECE 364 - Programming Methods for Machine Learning, Spring 2025, UIUC
  • undergraduate machine learning at UBC 2012, Nando de Freitas
  • CS 229 - Machine Learning - Stanford University (Autumn 2018)
  • CS 189/289A Introduction to Machine Learning, Prof Jonathan Shewchuk - UCBerkeley
  • CS 189/289A: Intro to Machine Learning - UC Berkeley - Spring 2026
  • CPSC 340: Machine Learning and Data Mining (2018) - UBC
  • CS391L Machine Learning, Spring 2025 - UT Austin
  • CS4780/5780 Machine Learning, Fall 2013 - Cornell University
  • CS4780/5780 Machine Learning, Fall 2018 - Cornell University (Youtube)
  • CSE474/574 Introduction to Machine Learning - SUNY University at Buffalo
  • CS 5350/6350 - Machine Learning, Spring 2024, University of Utah (Youtube)
  • ECE 4252/8803 Fundamentals of Machine Learning (FunML), Spring 2024 - Georgia Tech
  • ECE 5984 Introduction to Machine Learning, Spring 2015 - Virginia Tech
  • CSx824/ECEx242 Machine Learning, Bert Huang, Fall 2015 - Virginia Tech
  • STA 4273H - Large Scale Machine Learning, Winter 2015 - University of Toronto
  • CSC 2515 Introduction to Machine Learning, Amir-massoud Farahmand, Fall 2021, University of Toronto
  • ECE 421 Introduction to Machine Learning, Amir Ashouri, Winter 2019, University of Toronto
  • EECS 4404E/5327 Introduction to Machine Learning, Amir Ashouri, Fall 2019, York University
  • CS 480/680 Introduction to Machine Learning, Gautam Kamath, University of Waterloo (Spring 2021)
  • CS 480/680 Introduction to Machine Learning, Kathryn Simone, University of Waterloo (Fall 2024)
  • CS 485/685 Machine Learning, Shai Ben-David, University of Waterloo
  • STAT 441/841 Classification Winter 2017 , Waterloo
  • 10-605 - Machine Learning with Large Datasets, Fall 2016 - CMU
  • Information Theory, Pattern Recognition, and Neural Networks - University of Cambridge
  • Pattern Analysis (2018) - FAU (Class 2017) (Class 2016) (Class 2015) (Class 2009)
  • Pattern Recognition (2020-2021) - FAU (Class 2012-2013)
  • Beyond the Patterns (2020-2021) - FAU
  • Python and machine learning - Stanford Crowd Course Initiative
  • MOOC - Machine Learning Part 1a - Udacity/Georgia Tech (Part 1b Part 2 Part 3)
  • Pattern Recognition Class (2012)- Universität Heidelberg
  • Introduction to Machine Learning and Pattern Recognition - CBCSL OSU
  • Introduction to Machine Learning - IIT Kharagpur
  • Introduction to Machine Learning - IIT Madras
  • Pattern Recognition - IISC Bangalore
  • Pattern Recognition and Application - IIT Kharagpur
  • Pattern Recognition - IIT Madras
  • Machine Learning Summer School 2013 - Max Planck Institute for Intelligent Systems Tübingen
  • Machine Learning - Professor Kogan (Spring 2016) - Rutgers
  • CS273a: Introduction to Machine Learning (YouTube)
  • Machine Learning Crash Course 2015
  • COM4509/COM6509 Machine Learning and Adaptive Intelligence 2015-16
  • Introduction to Machine Learning - Spring 2018 - ETH Zurich
  • Machine Learning - Pedro Domingos- University of Washington
  • CSE 446/546 - Machine Learning, Spring 2020 - University of Washington (Videos)
  • Machine Learning (COMP09012)
  • Probabilistic Machine Learning 2020 - University of Tübingen
  • Statistical Machine Learning 2020 - Ulrike von Luxburg - University of Tübingen
  • COMS W4995 - Applied Machine Learning - Spring 2020 - Columbia University
  • Introductory Applied Machine Learning - Edinburgh University
  • Machine Learning for Engineers 2022 (YouTube)
  • 10-418 / 10-618 (Fall 2019) Machine Learning for Structured Data
  • ORIE 4741/5741: Learning with Big Messy Data - Cornell
  • Machine Learning in IoT
  • Stanford CS229M: Machine Learning Theory - Fall 2021
  • Intro to Machine Learning and Statistical Pattern Classification - Prof Sebastian Raschka
  • CMU's Multimodal Machine Learning course (11-777), Fall 2020
  • EE104: Introduction to Machine Learning - Stanford University
  • CPSC 330: Applied Machine Learning (2020) - UBC
  • Machine Learning 2013 - Nando de Freitas, UBC
  • Machine Learning, 2014-2015, University of Oxford
  • 10-702/36-702 - Statistical Machine Learning - Larry Wasserman, Spring 2016, CMU (Spring 2015)
  • 10-715 Advanced Introduction to Machine Learning - CMU (YouTube)
  • CS 281B - Scalable Machine Learning, Alex Smola, UC Berkeley
  • 100 Days of Machine Learning - CampusX (Hindi)
  • CampusX Data Science Mentorship Program 2022-23 (Hindi)
  • Statistical Machine Learning - S2023 - Benyamin Ghojogh
  • MIT 6.5940 EfficientML.ai Lecture, Fall 2023
  • TinyML - Tiny Machine Learning at UPenn
  • ECE 4760 (Digital Systems Design Using Microcontrollers) at Cornell for the Fall, 2022 (Spring 2021)
  • EfficientML.ai Lecture, Fall 2023, MIT 6.5940
  • SFU CMPT 727 Statistical Machine Learning, by Maxwell Libbrecht (Spring 2023) (Spring 2022)
  • UC Berkeley CS 189 / 289A Introduction to Machine Learning fall 2023, by Jennifer Listgarten & Jitendra Malik
  • UC Berkeley CS 189 Introduction to Machine Learning (CDSS offering) spring 2022, by Marvin Zhang
  • UC San Diego/edX DSE 220X Machine Learning Fundamentals, by Sanjoy Dasgupta
  • MIT 6.036 Introduction to Machine Learning spring 2019, by Leslie Kaelbling
  • LMU Munich Introduction to Machine Learning
  • CMU 15 388 / 15 688 Practical Data Science, by Zico Kolter (Fall 2019) (Spring 2018)
  • UW Madison CS 320 Data Programming II spring 2021, by Tyler R. Caraza-Harter
  • UC San Diego COGS9 Introduction to Data Science fall 2020, by Jason Fleischer
  • UCLA Stats 15 Introduction to Data Science fall 2022, by Miles Chen
  • UCLA Stats 21 Python and Other Technologies for Data Science spring 2024, by Miles Chen (Spring 2021)
  • UCLA Stats C161/C261 Introduction to Pattern Recognition and Machine Learning winter 2024, by Arash Amini (Winter 2023)
  • UCLA Stats 231C Theories of Machine Learning spring 2022, by Arash Amini
  • Princeton COS 511 Theoretical Machine Learning spring 2026, by Elad Hazan
  • MSU Machine Learning
  • Data Science for Dynamical Systems, by Oliver Wallscheid & Sebastian Peitz (YouTube)
  • Cambridge Statistical Learning in Practice 2021, by Alberto J. Coca
  • Data 8: The Foundations of Data Science - UC Berkeley (Spring 23) (Fall 22) (Spring 22) (Summer 17)
  • Data 144: Foundations of Data Science spring 2021 - Vassar College (Course materials)
  • CSE519 - Data Science Fall 2016 - Skiena, SBU
  • CS 109 Data Science, Harvard University (YouTube)
  • 6.0002 Introduction to Computational Thinking and Data Science - MIT OCW
  • Data 100: Principles and Techniques of Data Science - UC Berkeley (Fall 25) (Fall 24) (Spring 24) (Summer 19)
  • Data 102 - Spring 21- UC Berkeley (YouTube)
  • Distributed Data Analytics (WT 2017/18) - HPI University of Potsdam
  • Data Profiling and Data Cleansing (WS 2014/15) - HPI University of Potsdam
  • CS 229r - Algorithms for Big Data, Harvard University (Youtube)
  • Algorithms for Big Data - IIT Madras
  • Python Data Science with the TCLab (YouTube)
  • Foundations of Data Analysis (Fall 2020)- University of Utah
  • Introduction to Data Science (IDS) - Wil van der Aalst (RWTH Aachen University) (2021-2022 edition)
  • Machine Learning Class (Winter 2019-2020) - UniHeidelberg
  • Toronto ECE 1513 Introduction to Machine Learning winter 2026, by Ali Bereyhi

• Data Mining

  • CSEP 546, Data Mining - Pedro Domingos, Sp 2016 - University of Washington (YouTube)
  • CS 5140/6140 - Data Mining, Spring 2020, University of Utah by Prof. Jeff Phillips (Youtube)
  • CS 5140/6140 - Data Mining, Spring 2023, University of Utah by Prof. Ana Marasović (Youtube)
  • CS 5955/6955 - Data Mining, University of Utah (YouTube)
  • Statistics 202 - Statistical Aspects of Data Mining, Summer 2007 - Google (YouTube)
  • MOOC - Text Mining and Analytics by ChengXiang Zhai
  • Information Retrieval SS 2014, iTunes - HPI
  • MOOC - Data Mining with Weka
  • CS 290 DataMining Lectures
  • CS246 - Mining Massive Data Sets, Winter 2016, Stanford University (YouTube)
  • Information Retrieval - Spring 2018 - ETH Zurich
  • Information Retrieval - WS 2022/23 - Universität Freiburg
  • CAP6673 - Data Mining and Machine Learning - FAU(Video lectures)
  • CS 412 - Introduction to Data Mining - UIUC
  • CS 512 - Data Mining Principles - UIUC (YouTube)

• Probabilistic Graphical Modeling

  • CS 6190 - Probabilistic Modeling, Spring 2016, University of Utah
  • 10-708 - Probabilistic Graphical Models, Carnegie Mellon University
  • Probabilistic Graphical Models, Daphne Koller, Stanford University
  • Probabilistic Graphical Models, Spring 2018 - Notre Dame

• Deep Learning

  • Full Stack Deep Learning - Course 2022
  • Full Stack Deep Learning - Course 2021
  • NYU Deep Learning Spring 2020
  • NYU Deep Learning Spring 2021
  • 6.S191: Introduction to Deep Learning - MIT
  • 15.773: Hands-On Deep Learning Spring 2024 - MIT
  • 6.7960: Deep Learning Fall 2024 - MIT
  • Intro to Deep Learning and Generative Models Course - Prof Sebastian Raschka
  • Deep Learning CMU
  • CS231n Deep Learning for Computer Vision - Stanford University (Spring 2025) (Winter 2016 Andrej Karpathy)
  • Deep Learning: CS 182 Spring 2021
  • 10-414/714: Deep Learning Systems - CMU (Youtube)
  • 11-785: Introduction to Deep Learning - CMU (Lectures - YouTube-2024, Recitations - YouTube-2024)
  • Part 1: Practical Deep Learning for Coders, v3 - fast.ai
  • Part 2: Deep Learning from the Foundations - fast.ai
  • Deep learning at Oxford 2015 - Nando de Freitas
  • Self-Driving Cars — Andreas Geiger, 2021/22 (YouTube)
  • 6.S094: Deep Learning for Self-Driving Cars - MIT
  • CS294-129 Designing, Visualizing and Understanding Deep Neural Networks (YouTube)
  • CS230: Deep Learning - Autumn 2018 - Stanford University
  • CS230: Deep Learning - Autumn 2025 - Stanford University
  • STAT-157 Deep Learning 2019 - UC Berkeley
  • Deep Learning, Stanford University
  • MOOC - Neural Networks for Machine Learning, Geoffrey Hinton 2016 - Coursera
  • Stat 946 Deep Learning - University of Waterloo
  • EECS 298 Theory of Computational Neural Networks and Machine Learning (Fall 2020) - UC Irvine (YouTube)
  • ECE 1508 Applied Deep Learning - University of Toronto (Fall 2025) (Winter 2025) (Fall 2024)
  • ECE 1508 Reinforcement Learning - Fall 2025 - University of Toronto
  • Neural networks class - Université de Sherbrooke (YouTube)
  • DLCV - Deep Learning for Computer Vision - UPC Barcelona
  • DLAI - Deep Learning for Artificial Intelligence @ UPC Barcelona
  • Neural Networks and Applications - IIT Kharagpur
  • UVA DEEP LEARNING COURSE
  • Deep Learning - Winter 2020-21 - Tübingen Machine Learning
  • Geometric Deep Learning - AMMI
  • Math for Deep Learning — Andreas Geiger
  • Applied Deep Learning 2025 - TU Wien (2024) (2023) (2022) (2021) (2020)
  • Neural Networks: Zero to Hero - Andrej Karpathy
  • CIS 522 - Deep Learning - U Penn
  • UVA DEEP LEARNING COURSE
  • Deep Learning (Fall 2020) - FAU (Spring 2020) (Fall 2019) (Spring 2019) (Fall 2018) (Spring 2018)
  • Deep Learning (Fall 2020) - Georgia Tech
  • Mathematics of Deep Learning (2021) - FAU
  • CS7015 - Deep Learning - Prof. Mitesh M. Khapra - IIT Madras
  • ETH Zürich | Deep Learning in Scientific Computing 2023
  • Deep Learning François Fleuret
  • Applied Deep Learning Maziar Raissi
  • UC Berkeley CS 182 / 282a Deep Learning spring 2023, by Anant Sahai
  • CMSC 828W Foundations of Deep Learning (Fall 2020) - UMD (YouTube)
  • TUM IN2346 Introduction to Deep Learning Fall 2024, by Daniel Cremers (Summer 2023)
  • UT Austin - Advances in Deep Learning
  • HKU - Data 8014 Principles of Deep Representation Learning Fall 2025, by Yi Ma
  • Local Explanations for Deep Learning Models (Fall 2023)
  • CS 6966/5966 Interpretability of LLMs (Spring 2026) - University of Utah
  • Deep Graph Learning (DGL, 2025) - Prof. Islem Rekik - Imperial College London
  • Intuitive Deep Learning Series
  • Toronto ECE 1508 Applied Deep Learning winter 2026, by Ali BereyhiYoutube
  • UC Berkeley CS 182 / 282a Deep Learning fall 2025, by Gireeja Ranade & Anant SahaiYoutube
  • SNU M2177.43 Introduction to Deep Learning spring 2026, by Hyun Oh Song

• Reinforcement Learning

  • CS234: Reinforcement Learning - Spring 2024 - Stanford University (Winter 2019)
  • CSE 542: Reinforcement Learning - Spring 2024 - University of Washington
  • CSE 579: Reinforcement Learning - Autumn 2024 - University of Washington
  • CSC 2547: Introduction to Reinforcement Learning - Spring 2021 - University of Toronto (YouTube)
  • Introduction to reinforcement learning - UCL
  • Reinforcement Learning - IIT Madras (TA - Manav Mishra, TA - Prabhleen Kukreja, TA - Sandarbh Yadav , TA - Avik Kar)
  • Special topics in ML (Reinforcement Learning) IIT madras
  • CS885 Reinforcement Learning - Spring 2018 - University of Waterloo
  • CS 224R - Deep Reinforcement Learning- Stanford (YouTube)
  • CS 285 - Deep Reinforcement Learning- UC Berkeley
  • CS 294 112 - Reinforcement Learning
  • NUS CS 6101 - Deep Reinforcement Learning
  • ECE 8851: Reinforcement Learning
  • CS294-112, Deep Reinforcement Learning Sp17 (YouTube)
  • UCL Course 2015 on Reinforcement Learning by David Silver from DeepMind (YouTube)
  • DeepMind x UCL Reinforcement Learning Lecture Series 2021 - Hado van Hasselt, Diana Borsa, Matteo Hessel
  • Deep RL Bootcamp - Berkeley Aug 2017
  • Reinforcement Learning - IIT Madras
  • Reinforcement Learning Course at KTH (FDD3359 - 2022)
  • Reinforcement Learning Course at ASU, Spring 2022
  • CS 4789/5789: Introduction to Reinforcement Learning - Cornell
  • S20/IE613 - Online (Machine) Learning/ Bandit Algorithms
  • Reinforcement Learning - Fall 2021 chandar-lab
  • CMU 10 703 Deep Reinforcement Learning & Control fall 2022, by Katerina Fragkiadaki
  • ECE524 Foundations of Reinforcement Learning at Princeton University, Spring 2024
  • REINFORCEMENT LEARNING AND OPTIMAL CONTROL - Dimitri P. Bertsekas, ASU
  • CMU 16 745 Optimal Control and Reinforcement Learning spring by Zac Manchester
  • CMU 16 899 Adaptive Control and Reinforcement Learning fall 2020, by Changliu Liu
  • Jadavpur University, 2025: Introduction to Reinforcement Learning
  • EE675 (2024) Introduction to Reinforcement Learning Course | IIT Kanpur
  • Reinforcement Learning Course by Frédéric Godin - Concordia University
  • CS 285: Deep RL, 2023
  • Mathematical Foundations of Reinforcement Learning - WINDY Lab
  • Reinforcement Learning (HMC CS 181V)—Spring, 2020 - Neil Rhodes
  • Reinforcement Learning Course: Lectures (Summer 2023) by Paderborn University
  • CS292F (Spring 2021) Statistical Foundation of Reinforcement Learning - UCSD
  • Algorithmic Foundations of Interactive Learning - CMU

• Advanced Machine Learning

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

• Natural Language Processing

  • CS 224N -Natural Language Processing with Deep Learning - Stanford University (Lectures - Winter 2019) (Lectures - Winter 2021) (Lectures - Spring 2024)
  • CS 224N - Natural Language Processing, Stanford University (Lecture videos)
  • Stanford XCS224U: Natural Language Understanding I Spring 2023
  • CS388: Natural Language Processing - UT Austin
  • CS 124 - From Languages to Information - Stanford University
  • CS 6340/5340 - Natural Language Processing - University of Utah - Spring 2024 (Youtube)
  • CSE 447/517 - Natural Language Processing - University of Washington - Winter 2024
  • Neural Networks: Zero to Hero - Andrej Karpathy
  • fast.ai Code-First Intro to Natural Language Processing (Github)
  • MOOC - Natural Language Processing - Coursera, University of Michigan
  • Natural Language Processing at UT Austin (Greg Durrett)
  • CS224U: Natural Language Understanding - Spring 2019 - Stanford University
  • Deep Learning for Natural Language Processing, 2017 - Oxford University
  • Natural Language Processing - IIT Bombay
  • CMU Advanced NLP Fall 2024 (Lectures - Fall 2024) (Lectures - Fall 2021)
  • CMU Neural Nets for NLP 2021
  • Natural Language Processing - Michael Collins - Columbia University
  • CMU CS11-711 - Advanced Natural Language Processing (Lectures - Spring 2025)
  • CMU CS11-737 - Multilingual Natural Language Processing
  • UMass CS685: Advanced Natural Language Processing (Spring 2022)
  • Natural Language Processing (CMSC 470)
  • Stanford CS25 - Transformers United 2023
  • Natural Language Processing (IN2361) - TUM
  • Natural Language Processing (Spring 2024) - University of Utah
  • Multilingual NLP 2020 - CMU
  • Speech Technology - IIT Madras
  • Deep Learning for NLP (KAIST AI605 Fall 2021)

• Generative AI and LLMs

  • Stanford CS236: Deep Generative Models I 2023 I Stefano Ermon
  • CS 6785 - Deep Generative Models - Cornell Tech, Spring 2023)
  • ECE 498 / 598 - Deep Generative Models - UIUC, Fall 2025)
  • Mathematical Foundations of Generative AI - IIT Madras
  • Deep Generative Models - IISC
  • A Course on Generative AI - Diffusion Models - Israel Institute of Technology
  • MIT 6.S184 Flow Matching and Diffusion Models, 2025
  • Course on Diffusion Models for Generative AI - UT Austin
  • CS 492(C) Diffusion and Flow Models - Fall 2025 - KAIST (YouTube)
  • Stanford CS336 Language Modeling from Scratch I 2025 - Stanford (2026)
  • Stanford CME295 Transformers & LLMs - Autumn 2025 - Stanford
  • Stanford CME296: Diffusion & Large Vision Models
  • Introduction to large language models - IIT Madras
  • Build a Large Language Model (From Scratch) by Sebastian Raschka
  • Reinforcement Learning of Large Language Models - UCLA
  • WING NUS CS6101 Large Language Models (T2310)
  • CS 839: Foundation Models Fall 2025 - UW Madison (YouTube playlists)
  • CS 886: Recent Advances on Foundation Models Winter 2024 - University of Waterloo
  • UC Berkeley CS 194/294-196 Agentic AI Fall 2025 (YouTube, 2025) (Notes, 2024) (YouTube, 2024)
  • UC Berkeley CS 194/294-267 Understanding Large Language Models Foundations and Safety spring 2024, by Dawn Song & Dan Hendrycks
  • UC Berkeley CS 194/294-280 Advanced Large Language Model Agents Spring 2025 (YouTube)
  • Introduction to Large Language Models (LLMs), IIT Delhi
  • CMU 10423 S24: Generative AI Videos
  • CMU 10799 S26: Diffusion & Flow Matching
  • 6.S183: A Practical Introduction to Diffusion Models - MIT (YouTube, 2025 lectures)
  • The Principles of Diffusion Models - Vizuara
  • Building LLMs from scratch - Vizuara
  • Build DeepSeek from Scratch - Vizuara
  • Reasoning LLMs from Scratch - Vizuara
  • KAIST CS492(C): Diffusion and Flow Models (Fall 2025) (2024)
  • Mathematics of Generative Modelling (Spring 2024) - IIIT Hyderabad (2023)
  • NYCU Deep Generative Models 2025
  • COMPSCI 690L | Deep Generative Models | 2024 | Sajjad Amini
  • ECE1508: Deep Generative Models - Summer 2025 - UofT
  • 6.S980 Machine Learning for Inverse Graphics - Fall 2022 - MIT
  • 6.S087 - Foundation Models and Generative AI - MIT

• Computer Vision

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

• Time Series Analysis

  • 02417 Time Series Analysis
  • Applied Time Series Analysis

• Optimization

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

• Unsupervised Learning

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

• Misc Machine Learning Topics

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

---

Computer Networks

• CS 144 Introduction to Computer Networking - Stanford University, Fall 2013 (Lecture videos)
• Computer Networking: A Top-Down Approach
• Computer Communication Networks, Rensselaer Polytechnic Institute - Fall 2001 (Videos) (Slides)
• Audio/Video Recordings and Podcasts of Professor Raj Jain's Lectures - Washington University in St. Louis (YouTube)
• Computer Networks, Tanenbaum, Wetherall Computer Networks 5e - Video Lectures
• CSEP 561 - PMP Network Systems, Fall 2013 - University of Washington (Videos)
• CSEP 561 – Network Systems, Autumn 2008 - University of Washington (Videos)
• ECE/CS 438 - Communication Networks, Fall 2020 - UIUC
• Computer Networks - IIT Kharagpur
• Introduction to Data Communications 2013, Steven Gordon - Thammasat University, Thailand
• Introduction to Complex Networks - RIT
• Computer Networks and Internet Protocol - IIT Kharagpur
• Structural Analysis and Visualization of Networks
• Columbia ELEN E4703 Wireless Communications spring 2006, by Angel Lozano
• Columbia COMS W4119 Computer Networks fall 2004, by Vishal Misra
• Columbia ELEN E4710 An Introduction to Network Engineering fall 2004, by Dan Rubenstein (Videos)
• Data Communication - IIT Kharagpur
• Error Correcting Codes - IISC Bangalore
• Information Theory and Coding - IIT Bombay
• Complex Network : Theory and Application - IIT Kharagpur
• Advanced 3G and 4G Wireless Mobile Communications - IIT Kanpur
• Broadband Networks: Concepts and Technology - IIT Bombay
• Coding Theory - IIT Madras
• Digital Communication - IIT Bombay
• Digital Voice & Picture Communication - IIT Kharagpur
• Wireless Ad Hoc and Sensor Networks - IIT Kharagpur
• Internetworking with TCP/IP by Prof. Dr. Christoph Meinel - HPI
• CS798: Mathematical Foundations of Computer Networking - University of Waterloo
• CS 168 Introduction to the Internet: Architecture and Protocols, Fall 2022 - UC Berkeley (YouTube - Fall 2022) (Spring 2025)
• Advanced Topics in Communication Networks, Fall 2022 - ETH Zürich
• CS/ECE 438 Communication Networks [F23] - UIUC

---

Math for Computer Scientist

• Maths courses all topics covered - Khan Academy
• Calculus
  • 18.01 Single Variable Calculus, Fall 2006 - MIT OCW
  • 18.02 Multivariable Calculus, Fall 2007 - MIT OCW
  • 18.03 Differential Equations, Spring 2010 - MIT OCW
  • Highlights of Calculus - Gilbert Strang, MIT OCW
  • MAT123 Introduction to Calculus (Fall 2015) - Stony Brook
  • Vector Calculus for Engineers - HKUST
• Discrete Math
  • 6.042J - Mathematics for Computer Science, MIT OCW
  • 18.200 - Principles of Discrete Applied Mathematics, MIT OCW
  • CS 2050 Discrete Mathematics, Georgia Tech Videos - Su 2024
  • UC Berkeley Computer Science 70, Discrete Mathematics and Probability Theory - Fall 2025 (YouTube)
  • CSE 547 Discrete Mathematics, Prof Skiena, University of Stony Brook
  • Discrete Structures (Summer 2011) - Rutgers, The State University of New Jersey
  • Discrete Mathematics and Mathematical Reasoning 2015/16 - University of Edinburgh
  • Discrete Mathematical Structures - IIT Madras
  • Discrete Structures - Pepperdine University
  • CMU 21 228 Discrete Mathematics spring 2021, by Po-Shen Loh
  • COMP2804: Discrete Structures II
• Probability & Statistics
  • Statistics - CrashCourse
  • 6.041 Probabilistic Systems Analysis and Applied Probability - MIT OCW
  • Stanford CS109 Introduction to Probability for Computer Scientists I 2022 I Chris Piech
  • MIT RES.6-012 Introduction to Probability, Spring 2018 - MIT
  • Statistics 110 - Probability - Harvard University
  • Statistical Rethinking 2026 - Richard McElreath
  • STAT 2.1x: Descriptive Statistics | UC Berkeley
  • STAT 2.2x: Probability | UC Berkeley
  • MOOC - Statistics: Making Sense of Data, Coursera
  • MOOC - Statistics One - Coursera
  • Probability and Random Processes - IIT Kharagpur
  • MOOC - Statistical Inference - Coursera
  • 131B - Introduction to Probability and Statistics, UCI
  • STATS 250 - Introduction to Statistics and Data Analysis, UMichigan
  • Sets, Counting and Probability - Harvard
  • Opinionated Lessons in Statistics (Youtube)
  • Statistics - Brandon Foltz
  • Statistical Rethinking: A Bayesian Course Using R and Stan (Lectures) (Book)
  • 02402 Introduction to Statistics E12 - Technical University of Denmark (F17)
  • Engineering Probability (ECSE-2500) - RPI
  • Purdue ECE302 Introduction to Probability for Data Science
  • Undergraduate Probability with Professor Roman Vershynin
  • High-Dimensional Probability
  • Mathematical Statistics - 2024 (YouTube-2020)
  • Bayesian Data Analysis
  • Bayesian Machine Learning and Information Processing (YouTube-2021/22) (YouTube-2020/21)
  • Markov Processes - Spring 2023
  • Measure Theoretic Probability
  • Causal Inference Course - Brady Neal
  • Causal Inference -- Online Lectures (M.Sc/PhD Level)
  • Machine Learning & Causal Inference: A Short Course
  • Causal Inference Jonas Peters
  • UIUC ECE 534 Random Processes fall 2020 - Ilan Shomorony
  • ISyE 320 Simulation and Probabilistic Modeling spring 2022, by Qiaomin Xie - University of Wisconsin-Madison
  • Cambridge Principles of Statistics 2020, by Alberto J. Coca
  • UC Berkeley STAT 150 Stochastic Processes spring 2021, by Brett Kolesnik
  • UIUC Math 564 Applied Stochastic Processes fall 2016, by Kay Kirkpatrick
  • CS/ECE 561 - Probability and Info Theory in Machine Learning
  • UCLA Stats 10 Introduction to Statistical Reasoning summer 2022, by Miles Chen
  • UCLA Stats 101C Statistical Models and Data Mining summer 2022, by Miles Chen
  • UCLA Stats 102A Introduction to Computational Statistics with R winter 2024, by Miles Chen
  • UCLA Stats 102B Computation and Optimization for Statistics spring 2024, by Miles Chen
  • UCLA Stats 102C Introduction to Monte Carlo Methods fall 2023, by Miles Chen
  • UCLA Stats 200B Theoretical Statistics winter 2024, by Arash Amini (Winter 2023)
  • UCLA Stats 200C High-dimensional Statistics spring 2022, by Arash Amini (Spring 2021)
  • UCLA Stats 203 Large Sample Theory fall 2021, by Jingyi Jessica Li (Fall 2020)
  • UCSD Math 280 Probability Theory and Stochastic Processes, by Todd Kemp (YouTube)
  • METU EE 531 Probability and Stochastic Processes, by Elif Uysal
  • 6.262 Discrete Stochastic Processes - MIT OCW
  • 18.650 Statistics for Applications - MIT OCW
  • STAT240 - Robust Statistics - UC Berkeley
  • MAS575 Combinatorics at KAIST
  • Probabilistic method, KAIST 2020
• Linear Algebra