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
  • 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
• Blockchain Development
• Misc

Courses

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Introduction to Computer Science

• 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)
• 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 (Lecture Videos)
• CS 106L - Standard C++ Programming(Lecture Videos)
• CS 106X - Programming Abstractions in C++ (Lecture Videos)
• CS 107 - Programming Paradigms, Stanford University
• CmSc 150 - Introduction to Programming with Arcade Games, Simpson College
• 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 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
• 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 - Richard Buckland - 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(Youtube)
• 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
• 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
• 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 6.854 / 18.415 Advanced Algorithms spring 2016, by Ankur Moitra
• CMU 10 801 Advanced Optimization and Randomized Algorithms spring 2014, by Suvrit Sra and Alex Smola
• UC Santa Cruz CSE 202 Combinatorial Algorithms spring 2021, by Seshadhri Comandur
• UC Santa Cruz CSE 104, 204 Computational Complexity fall 2020, spring 2022, by Seshadhri Comandur
• UC Santa Cruz CSE 290A Randomized Algorithms spring 2020, by Seshadhri Comandur

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Systems Programming

• 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

  • ECS 150 - Operating Systems and Systems Programming - Fall 2020 - Joël Porquet-Lupine - UC Davis
  • CS124 Operating Systems - California Institute of Technology, Fall 2018 - Youtube
  • CS 162 Operating Systems and Systems Programming, Spring 2015 - University of California, Berkeley
  • 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
  • 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

• 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

• 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)
• 15-721 - Database Systems, CMU (YouTube-2017, YouTube-2016)
• 15-721 Advanced Database Systems (Spring 2019) - CMU
• 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

  • 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
  • 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

• 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 spring 2018, fall 2022, by Wen-mei Hwu, Sanjay Patel
  • 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

• 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
• CS 188 - Introduction to Artificial Intelligence, UC Berkeley - 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 4804: Introduction to Artificial Intelligence, Fall 2016
• CS 5804: Introduction to Artificial Intelligence, Spring 2015
• 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)
  • 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
  • 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
  • CPSC 340: Machine Learning and Data Mining (2018) - UBC
  • 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 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
  • 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
  • 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
  • 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
  • EfficientML.ai Lecture, Fall 2023, MIT 6.5940
  • SFU CMPT 727 Statistical Machine Learning spring 2022, 2023, by Maxwell Libbrecht
  • 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
  • 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
  • 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
  • MSU Machine Learning
  • Data Science for Dynamical Systems, by Oliver Wallscheid & Sebastian Peitz
  • 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 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)

• 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
  • CAP6673 - Data Mining and Machine Learning - FAU(Video lectures)
  • CS 412 - Introduction to Data Mining - UIUC

• 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
  • Intro to Deep Learning and Generative Models Course - Prof Sebastian Raschka
  • Deep Learning CMU
  • CS231n Deep Learning for Computer Vision - Winter 2016 Andrej Karpathy - Stanford University
  • 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
  • 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)
  • 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 2022 - TU Wien
  • Neural Networks: Zero to Hero - Andrej Karpathy
  • CIS 522 - Deep Learning - U Penn
  • UVA DEEP LEARNING COURSE
  • Deep Learning (Fall 2020) - Georgia Tech
  • CS7015 - Deep Learning - Prof. Mitesh M. Khapra - IIT Madras
  • ETH Zürich | Deep Learning in Scientific Computing 2023
  • Applied Deep Learning Maziar Raissi
  • UC Berkeley CS 182 / 282a Deep Learning spring 2023, by Anant Sahai
  • Foundations of Deep Learning - UMD
  • TUM IN2346 Introduction to Deep Learning Fall 2024, by Daniel Cremers (Summer 2023)

• 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
  • Introduction to reinforcement learning - UCL
  • Reinforcement Learning - IIT Madras
  • CS885 Reinforcement Learning - Spring 2018 - University of Waterloo
  • 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)
  • 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

• 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

• 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 CS11-737 - 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
  • CS 886: Recent Advances on Foundation Models Winter 2024 - University of Waterloo
  • UC Berkeley CS 194/294-196 Large Language Model Agents Fall 2024, by Dawn Song & Xinyun Chen (YouTube playlist)
  • UC Berkeley CS 194/294-267 Understanding Large Language Models Foundations and Safety spring 2024, by Dawn Song & Dan Hendrycks
  • Introduction to Large Language Models (LLMs), IIT Delhi
  • Natural Language Processing (Spring 2024) - University of Utah

• Generative AI

  • Stanford CS236: Deep Generative Models I 2023 I Stefano Ermon
  • CS 6785 - Deep Generative Models - Cornell Tech, Spring 2023)
  • MIT 6.S184 Flow Matching and Diffusion Models, 2025

• Computer Vision

  • CS 231n - Convolutional Neural Networks for Visual Recognition, Stanford University
  • 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)
  • CAP5415 Computer Vision - UCF Fall 2023
  • CAP6412 Advanced Computer Vision - UCF Spring 2024 (Youtube)
  • Advanced Deep Learning for Computer vision (ADL4CV) (IN2364) - TU Munich (Youtube)
  • Computer Vision III: Detection, Segmentation and Tracking (CV3DST) (IN2375) - TU Munich

• 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
  • EE364a: Convex Optimization I - Stanford University
  • 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)
  • University of Twente Discrete Optimization, by Marc Uetz (Fall 2020)
  • 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

• Unsupervised Learning

  • CS294 Deep Unsupervised Learning Spring 2024
  • Deep Unsupervised Learning -- Berkeley Spring 2020
  • CS294-158 Deep Unsupervised Learning SP19
  • 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
  • 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
  • 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)
  • CSCE 585 - Machine Learning Systems, University of South Carolina (YouTube-2020)

---

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
• 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)
• Advanced Topics in Communication Networks, Fall 2022 - ETH Zürich

---

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
  • Computer Science 70, 001 - Spring 2015
  • 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
• 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
  • 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 2023, by Arash Amini
  • 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)
• Linear Algebra
  • Mathematical Foundations of Machine Learning (Fall 2021) - University of Chicago - Rebecca Willett
  • 18.06 - Linear Algebra, Prof. Gilbert Strang, MIT OCW
  • 18.065 Matrix Methods in Data Analysis, Signal Processing, and Machine Learning - MIT OCW
  • University of Wisconsin-Madison ECE/CS/ME 532 Matrix Methods in Machine Learning fall 2017, by Laurent Lessard
  • Linear Algebra (Princeton University)
  • MOOC: Coding the Matrix: Linear Algebra through Computer Science Applications - Coursera
  • CS 053 - Coding the Matrix - Brown University (Fall 14 videos)
  • Linear Algebra Review - CMU
  • A first course in Linear Algebra - N J Wildberger - UNSW
  • INTRODUCTION TO MATRIX ALGEBRA
  • Computational Linear Algebra - fast.ai (Github)
  • ENGR108: Introduction to Applied Linear Algebra—Vectors, Matrices, and Least Squares - Stanford University
  • MIT 18.S096 Matrix Calculus For Machine Learning And Beyond
  • Cornell MATH 2940 Linear Algebra for Engineers spring 2009, by Andy Ruina
• 10-600 Math Background for ML - CMU
• MIT 18.065 Matrix Methods in Data Analysis, Signal Processing, and Machine Learning
• Direct Methods for Sparse Linear Systems - Prof Tim Davis - UFL
• 36-705 - Intermediate Statistics - Larry Wasserman, CMU (YouTube)
• Combinatorics - IISC Bangalore
• Advanced Engineering Mathematics - Notre Dame
• Statistical Computing for Scientists and Engineers - Notre Dame
• Statistical Computing, Fall 2017 - Notre Dame
• Statistics 243 Introduction to Statistical Computing, Fall 2015 - UC Berkeley (Notes-2015) (YouTube-2014) (Notes-2014) (YouTube-2013)
• Mathematics for Machine Learning, Lectures by Ulrike von Luxburg - Tübingen Machine Learning
• Essential Mathematics for Machine Learning- July 2018 - IIT Roorkee - YouTube Lectures
• Numerics of Machine Learning (Winter 2022/23) - Tübingen Machine Learning
• Nonlinear Dynamics and Chaos - Steven Strogatz, Cornell University
• Nonlinear Dynamics & Chaos - Virginia Tech
• An introduction to Optimization on smooth manifolds (with book) - EPFL
• Math Modelling
• Large-Scale Convex Optimization: Algorithms & Analyses via Monotone Operators by Ernest Ryu and Wotao Yin
• An Overview of Variational Analysis 2021 by Tyrrell Rockafellar
• UW AMATH 584 Applied Linear Algebra & Numerical Analysis by Nathan Kutz
• UW AMATH 584 Applied Linear Algebra & Introductory Numerical Analysis fall 2005, by Loyce Adams
• Stanford CME 206 Introduction to Numerical Methods for Engineering spring 2005, by Charbel Farhat
• Stanford CME 200 Linear Algebra with Application to Engineering Computations autumn 2004, by Margot Gerritsen
• Stanford CME 302 Numerical Linear Algebra autumn 2007, by Gene Golub
• TUe Numerical Linear Algebra 2021, by Martijn Anthonissen
• Numerical Linear Algebra fall 2018, by Jaegul Choo
• MIT 6.S955 Applied Numerical Algorithms fall 2023, by Justin Solomon
• UC Berkeley Math 55 Discrete Mathematics fall 2021, by Nikhil Srivastava
• Fundamental Mathematics for Robotics spring 2020, by Ken Tomiyama
• Short Course on Casual Inference, by Sanjay Shakkottai
• UCLA STAT 100C Linear Models spring 2023, by Arash Amini
• MSU Math for Computing
• Mathematics of Data Science - ETH Zurich

---

Web Programming and Internet Technologies

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

---

Theoretical CS and Programming Languages

• MOOC - Compilers - Stanford University
• CS 6120: Advanced Compilers: The Self-Guided Online Course - Cornell University
• CS 164 Hack your language, UC Berkeley (Lectures - Youtube)
• Theory of computation - Shai Simonson
• CS 173 Programming Languages, Brown University (Book)
• CS Theory Toolkit at CMU 2020
• CS 421 - Programming Languages and Compilers, UIUC
• CSC 253 - CPython internals: A ten-hour codewalk through the Python interpreter source code, University of Rochester
• CSE341 - Programming Languages, Dan Grossman, Spring 2013 - University of Washington
• CSEP 501 - Compiler Construction, University of Washington (Lectures - Youtube)
• CSEP 505 Programming Languages, Winter 2015 - University of Washington
• DMFP - Discrete Mathematics and Functional Programming, Wheaton College
• CS 374 - Algorithms & Models of Computation (Fall 2014), UIUC (Lecture videos)
• 6.045 Automata, Computability, and Complexity, MIT (Lecture Videos)
• MOOC - Automata - Jeffrey Ullman - Coursera
• CS581 Theory of Computation - Portland State University (Lectures - Youtube)
• Theory of Computation - Fall 2011 UC Davis
• TDA555 Introduction to Functional Programming - Chalmers University of Technology (Lectures - YouTube)
• Ryan O'Donnell Theoretical Computer Science Talks
• Philip Wadler Haskell lecture recordings
• Functional Programming (2021) - University of Nottingham
• Functional Programming - University of Edinburgh - 2016-17
• MOOC - Functional Programming Principles in Scala by Martin Odersky
• CS294 - Program Synthesis for Everyone
• MOOC - Principles of Reactive Programming, Scala - Coursera
• Category Theory for Programmers, 2014 - Bartosz Milewski (YouTube)
• Oregon Programming Languages Summer School (Proof theory, type theory, category theory, verification)
  • 2012 Lectures
  • 2013 Lectures
  • 2014 Lectures
  • 2015 Lectures
  • 2016 Lectures
  • Latest YT playlists
• Inf1 - Computation and Logic 2015 - University of Edinburgh
• INFORMATICS 1 - FUNCTIONAL PROGRAMMING - University of Edinburgh (Videos)
• Compiler Design - IISC Bangalore
• Compiler Design - IIT Kanpur
• Principles of Programming Languages - IIT Delhi
• Principles of Compiler Design - IISC Bangalore
• Functional Programming in Haskell - IIT Madras
• Theory of Computation - IIT Kanpur
• Theory of Automata, Formal Languages and Computation - IIT Madras
• Theory of Computation - IIT Kanpur
• Logic for CS - IIT Delhi
• Principles of Compiler Design - Swarthmore College
• Undergrad Complexity Theory at CMU
• Graduate Complexity Theory at CMU
• Great Ideas in Theoretical Computer Science at CMU
  • Another link
• Analysis of Boolean Functions at CMU
• Theoretical Computer Science (Bridging Course)(Tutorial) - SS 2015
• Languages & Translators - UCLouvain LINFO2132
• Compiler Design by Sorav Bansal
• OCaml Programming: Correct + Efficient + Beautiful
• Columbia IEOR E4004 Introduction to Operations Research: Deterministic Models summer 2005, by Jay Sethuraman
• Columbia IEOR E4106 Introduction to Operations Research: Stochastic Models spring 2005, by Ward Whitt
• Columbia ELEN E6711 Stochastic Models in Information Systems fall 2005, by Yuliy Barsyhnikov
• Columbia ELEN E6717 Information Theory fall 2003, by Vittorio Castelli
• CMU 21 738 Extremal Combinatorics spring 2020, by Po-Shen Loh
• JHU Domain-Specific Languages (DSL) Class (Summer 2018)

---

Embedded Systems

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

---

Real time system evaluation

• Performance evaluation of Computer systems - IIT Madras
• Real Time systems - IIT Karaghpur
• EE 380 Colloquium on Computer Systems - Stanford University
• System storages - IISc Bangalore
• High Performance Computing - IISC Bangalore
• 2023 High Performance Computing Course Prof Dr - Ing Morris Riedel (2022)
• High Performance Computing | Udacity
• UCLA Stats 205 Hierarchical Linear Models spring 2024, by Jingyi Jessica Li
• UF EML 6934 Optimal Control spring 2012, by Anil V. Rao

---

Computer Organization and Architecture

• Computer Organization
  • How Computers Work - Aduni
  • CS 61C - Machine Structures, UC Berkeley Spring 2015
  • 6.004 - Computation Structures Spring 2013, MIT
  • CS/ECE 3810 Computer Organization, Fall 2015, , University of Utah (YouTube)
  • Digital Computer Organization - IIT Kharagpur
  • Computer Organization - IIT Madras
  • CS-224 - Computer Organization, 2009-2010 Spring, Bilkent University (YouTube playlist)
  • INFORMATICS 2C - INTRODUCTION TO COMPUTER SYSTEMS (AUTUMN 2016) - University of Edinburgh
• Computer Architecture
  • 18-447 - Introduction to Computer Architecture, CMU (Lectures - YouTube - Fall 15)
  • CSEP 548 - Computer Architecture Autumn 2012 - University of Washington
  • CS/ECE 6810 Computer Architecture, Spring 2016, University of Utah (YouTube)
  • MOOC - Computer Architecture, David Wentzlaff - Princeton University/Coursera
  • Computer Architecture - ETH Zürich - Fall 2019
  • Digital Circuits and Computer Architecture - ETH Zurich - Spring 2017
  • Computer Architecture - IIT Delhi
  • Computer Architecture - IIT Kanpur
  • Computer Architecture - IIT Madras
  • High Performance Computer Architecture - IIT Kharagpur
  • BE5B35APO - Computer Architectures, Spring 2022, CTU - FEE (YouTube - Spring 2022) (RISC-V simulator - QtRvSim)
  • CS773: Computer Architecture for Performance and Security - IIT Bombay
• Parallel Computer Architecture
  • 15-418 - Parallel Computer Architecture and Programming, CMU (Lecture Videos)
  • CS 267 Applications of Parallel Computers, Spring 18 - UC Berkeley (YouTube-Spring 18) (Notes-Spring 16) (YouTube-Spring 16)
  • MOOC - Heterogeneous Parallel Programming - Coursera
  • ECE 498AL - Programming Massively Parallel Processors
  • Parallel Computing - IIT Delhi
  • Parallel Architectures 2012/13- University of Edinburgh
• Digital Systems Design
  • ELEC2141 Digital Circuit Design, UNSW
  • Digital Systems Design - IIT Kharagpur
  • Digital Design Course - 2015 - UNCC
• CS1 - Higher Computing - Richard Buckland UNSW
• MOOC - From NAND to Tetris - Building a Modern Computer From First Principles (YouTube)
• System Validation, TU Delft
• High Performance Computing - IISC Bangalore
• Introduction to ARM - Open SecurityTraining
• Intro x86 (32 bit) - Open SecurityTraining
• Intermediate x86 (32 bit) - Open SecurityTraining
• Design of Digital Circuits - ETH Zürich - Spring 2019
• Onur Mutlu @ TU Wien 2019 - Memory Systems
• Memory Systems Course - Technion, Summer 2018
• UC Berkeley EECS16A Designing Information Devices and Systems I summer 2020, by Grace Kuo, Panos Zarkos, Urmita Sikder
• UC Berkeley EECS 16B Designing Information Devices and Systems II fall 2020, by Seth Sanders, Miki Lustig
• ELEN E4896 - MUSIC SIGNAL PROCESSING - Spring 2016 - Columbia
• Columbia ELEN E6820 Speech and Audio Processing spring 2006, by Dan Ellis
• CMU 11 751 / 18 781 Speech Recognition and Understanding fall 2023, by Shinji Watanabe (Fall 2022)
• CMU 11 492 Speech Processing fall 2021, by Alan W. Black

---

Security

• Internet Security (WT 2018/19) - HPI University of Potsdam
• 6.1600 Foundations of Computer Security - MIT Fall 2023
• 6.858 Computer Systems Security - MIT OCW
• CS 253 Web Security - Stanford University
• CS 161: Computer Security, UC Berkeley (Videos)
• 6.875 - Cryptography - Fall 2021 - MIT (Spring 2018)
• CSEP590A - Practical Aspects of Modern Cryptography, Winter 2011 - University of Washington (Videos)
• CS461/ECE422 - Computer Security - University of Illinois at Urbana-Champaign (Videos)
• Introduction to Cryptography, Christof Paar - Ruhr University Bochum, Germany
• ECS235B Foundations of Computer and Information Security - UC Davis
• CIS 4930/ CIS 5930 - Offensive Computer Security, Florida State University
• Introduction to Information Security I - IIT Madras
• Information Security - II - IIT Madras
• Introduction to Cryptology - IIT Roorkee
• Cryptography and Network Security - IIT Kharagpur
• 18-636 Browser Security, Stanford
• Internet Security - Weaknesses and Targets (WT 2015/16) (WT 2012/13 (YouTube))
• IT Security, Steven Gordon - Thammasat University, Thailand
• Security and Cryptography, Steven Gordon - Thammasat University, Thailand
• MOOC - Cryptography - Coursera
• MOOC - Intro to Information Security - Udacity
• ICS 444 - Computer & Network Security
• Privacy and Security in Online Social Networks - IIT Madras
• Malware Dynamic Analysis - Open SecurityTraining (YouTube)
• CSN09112 - Network Security and Cryptography - Bill Buchanan - Edinburgh Napier
• CSN10107 - Security Testing and Network Forensics - Bill Buchanan - Edinburgh Napier
• CSN11123 - Advanced Cloud and Network Forensics - Bill Buchanan - Edinburgh Napier
• CSN11117 - e-Security - Bill Buchanan - Edinburgh Napier
• CSN08704 - Telecommunications - Bill Buchanan - Edinburgh Napier
• CSN11128 - Incident Response and Malware Analysis - Bill Buchanan - Edinburgh Napier
• Internet Security for Beginners by Dr. Christoph Meinel - HPI
• Offensive Security and Reverse Engineering, Chaplain University by Ali Hadi
• Computer Systems Security, Fall 2020, Vinod Ganapathy, IISc Bangalore
• UC Berkeley CS 161 Computer Security, Summer 2021, by Nicholas Ngai and Peyrin Kao
• UCSD CS291A Differential Privacy Fall 2021, by Yu-Xiang Wang (Youtube)
• Zero Knowledge Proofs MOOC, UC Berkeley RDI Center on Decentralization & AI

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Computer Graphics

• ECS 175 - Computer Graphics, Fall 2009 - UC Davis
• 6.837 - Computer Graphics - Spring 2017 - MIT
• 6.838 - Shape Analysis - Spring 2017- MIT
• Introduction to Computer Graphics - IIT Delhi
• Computer Graphics - IIT Madras
• Computer Graphics 2012, Wolfgang Huerst, Utrecht University
• CS 5630/6630 - Visualization, Fall 2016, University of Utah (Lectures - Youtube)
• Advanced Visualization UC Davis
• Computer Graphics Fall 2011, Barbara Hecker
• Ray Tracing for Global Illumination, UCDavis
• Rendering / Ray Tracing Course, SS 2015 - TU Wien introduction/id389259246))
• Computational Geometry - IIT Delhi
• CS 468 - Differential Geometry for Computer Science - Stanford University (Lecture videos)
• CMU 15-462/662: Computer Graphics
• UC Berkeley CS184/284A Computer Graphics and Imaging Spring 2022, by Ren Ng (YouTube playlist)

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Image Processing and Computer Vision

• Digital Image Processing - IIT Kharagpur
• CS 543 - Computer Vision – Spring 2017 (Recordings)
• CAP 5415 - Computer Vision - University of Central Florida(Video Lectures)
• EE637 - Digital Image Processing I - Purdue University (Videos - Sp 2011,Videos - Sp 2007)
• Computer Vision I: Variational Methods - TU München (YouTube)
• Computer Vision II: Multiple View Geometry (IN2228), SS 2016 - TU München (YouTube)
• EENG 512/CSCI 512 - Computer Vision - Colorado School of Mines
• Computer Vision for Visual Effects - RPI (YouTube)
• Introduction to Image Processing - RPI (YouTube)
• CAP 6412 - Advanced Computer Vision - University of Central Florida(Video lectures) (Spring 2018)
• Digital Signal Processing - RPI
• UC Berkeley EE 123 Digital Signal Processing fall 2003, by Avideh Zakhor
• UC Berkeley EE 225B Digital Image Processing spring 2006, by Avideh Zakhor
• Advanced Vision 2014 - University of Edinburgh
• Photogrammetry Course - 2015/16 - University of Bonn, Germany
• MOOC - Introduction to Computer Vision - Udacity
• ECSE-4540 - Intro to Digital Image Processing - Spring 2015 - RPI
• Machine Learning for Computer Vision - Winter 2017-2018 - UniHeidelberg
• High-Level Vision - CBCSL OSU
• Advanced Computer Vision - CBCSL OSU
• Introduction to Image Processing & Computer Vision - CBCSL OSU
• Machine Learning for Computer Vision - TU Munich
• Biometrics - IIT Kanpur
• Quantitative Big Imaging 2019 ETH Zurich
• Multiple View Geometry in Computer Vision
• Modern C++ Course For CV (2020) - University of Bonn
• Photogrammetry 1 Course – 2020 - University of Bonn
• Photogrammetry II Course 2020/21 - University of Bonn
• 3D Computer Vision - National University of Singapore

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Computational Physics

• Statistics and Machine Learning for Astronomy
• Astronomical data analysis using Python 2021 - NRC IUCAA
• SPARC Workshop on Machine Learning in Solar Physics and Space Weather - CESSI IISER Kolkata
• Data-Driven Methods and Machine Learning in Atmospheric Sciences - IISC
• Computational Astrophysics - AstroTwinCoLo, 2015
• Astroinformatics 2019 Conference - Caltech
• Space Science with Python - Astroniz
• Computational Physics Course in Python, Rutgers 2021
• Landau Computational Physics Course
• Statistical Methods and Machine Learning in High Energy Physics
• Physics Informed Machine Learning by Steve Brunton
• Physics-informed machine learning meets engineering seminar series
• Physics Informed Machine Learning Workshop
• Jake VanderPlas: Machine learning in Astronomy python tutorial

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Computational Biology

• ECS 124 - Foundations of Algorithms for Bioinformatics - Dan Gusfield, UC Davis (YouTube)
• CSE549 - Computational Biology - Steven Skiena - 2010 SBU
• 7.32 Systems Biology, Fall 2014 - MIT OCW
• 6.802J/ 6.874J Foundations of Computational and Systems Biology - MIT OCW
• 6.S897 Machine Learning For Healthcare
• 6.047/6.878 Machine Learning for Genomics Fall 2020 - MIT
• 6.874 MIT Deep Learning in Life Sciences - Spring 2021 - MIT
• 6.047/6.878 Public Lectures on Computational Biology: Genomes, Networks, Evolution - MIT
• Bio 84 - Your Genes and Your Health, Stanford University
• BioMedical Informatics 231 Computational Molecular Biology, Stanford University
• BioMedical Informatics 258 Genomics, Bioinformatics & Medicine, Stanford University
• 03-251: Introduction to Computational Molecular Biology - Carnegie Mellon University
• 03-712: Biological Modeling and Simulation - Carnegie Mellon University
• MOOC - Bioinformatics Algorithms: An Active Learning Approach - UC San Diego/Coursera
• Neural Networks and Biological Modeling - Lecturer: Prof. Wulfram Gerstner - EPFL
• Video Lectures of Wulfram Gerstner: Computational Neuroscience - EPFL
• An Introduction To Systems Biology
• Introduction to Bioinformatics, METUOpenCourseWare
• MOOC - Algorithms for DNA Sequencing, Coursera
• Frontiers of Biomedical Engineering with W. Mark Saltzman - Yale
• NOC:Computational Systems Biology - IIT Madras
• NOC:BioInformatics:Algorithms and Applications - IIT Madras
• Data Science and AI for Neuroscience Summer School - Caltech Neuroscience
• Neuroscience 299: Computing with High-Dimensional Vectors - Fall 2021 - UC Berkeley
• BIO410/510 Bioinformatics - California State University, Monterey Bay
• BIO412: Comparative Genomics - California State University, Monterey Bay
• CENG 465 - Introduction to Bioinformatics (Spring 2020-2021)
• UCLA Stats M254 Statistical Methods in Computational Biology spring 2024, by Jingyi Jessica Li
• Cell and Molecular Biology for Engineers ETH Zurich
• Statistical Models in Computational Biology
• ETH Zürich Statistical Models in Computational Biology spring 2018, by Niko Beerenwinkel
• UC Berkeley CS 198-96 Introduction to Neurotechnology fall 2020
• MLCB24 - Machine Learning in Computational Biology Fall 2024

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Quantum Computing

• 15-859BB: Quantum Computation and Quantum Information 2018 - CMU (Youtube)
• Quantum Computation and Information at CMU
• Ph/CS 219A Quantum Computation - Prof Preskill - Caltech
• Quantum Mechanics and Quantum Computation - Umesh Vazirani
• Introduction to quantum computing course 2022 - NYU
• Phys 1470 - Foundations of Quantum Computing and Quantum Information - U of Pittsburgh
• Introduction to Quantum Computing From a Layperson to a Programmer in 30 Steps (EE225 SJSU)
• Quantum Computing Hardware and Architecture (EE274 SJSU)
• Quantum Physics for Non-Physicists 2021 - ETH Zurich (2020)
• Introduction to Quantum Computing and Quantum Hardware - Qiskit
• Understanding Quantum Information and Computation - Qiskit
• Lectures in Quantum Computation and Quantum Information (IIT Madras)
• Quantum Information and Computing by Prof. D.K. Ghosh
• Quantum Computing by Prof. Debabrata Goswami
• The Building Blocks of a Quantum Computer: Part 1 - TU Delft
• The Building Blocks of a Quantum Computer: Part 2 - TU Delft
• Quantum Cryptography - TU Delft
• Introduction to Quantum Information
• Quantum Computing for Everyone -- Part 1 (Part 2)
• Quantum Computer Systems – UChicago
• Quantum computing for the determined - Michael Nielsen
• Quantum Computing

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Robotics and Control

• ROB 101: Computational Linear Algebra - University of Michigan (Youtube - Fall 2021)
• ROB 102: Introduction to AI and Programming - University of Michigan
• ROB 311: How to Build Robots and Make Them Move - University of Michigan
• ROB 320: Robot Operating Systems - University of Michigan
• ROB 501: Mathematics for Robotics - University of Michigan (Youtube)
• ROB 530 MOBILE ROBOTICS at U of Michigan - WINTER 2022 -- Instructor: Maani Ghaffari
• Autorob Winter 2022 - University of Michigan
• DeepRob Winter 2023 - University of Michigan
• CS 223A - Introduction to Robotics, Stanford University
• 6.832 Underactuated Robotics - MIT OCW
• CS287 Advanced Robotics at UC Berkeley Fall 2019 -- Instructor: Pieter Abbeel
• CS 287 - Advanced Robotics, Fall 2011, UC Berkeley (Videos)
• CMU 16-715 Robot Dynamics 2022 - CMU
• CMU 16-745 Optimal Control 2024 - CMU (Lecture notebooks) (YouTube-2023) (YouTube-2022)
• CS235 - Applied Robot Design for Non-Robot-Designers - Stanford University
• Lecture: Visual Navigation for Flying Robots (YouTube)
• CS 205A: Mathematical Methods for Robotics, Vision, and Graphics (Fall 2013)
• Robotics 1, Prof. De Luca, Università di Roma (YouTube)
• Robotics 2, Prof. De Luca, Università di Roma (YouTube)
• Robot Mechanics and Control, SNU
• Introduction to Robotics Course - UNCC
• SLAM Lectures
• CSE 478 – Autonomous Robotics – Winter 2025 - University of Washington (Winter 2024)
• ME 597 – Autonomous Mobile Robotics – Fall 2014
• ME 780 – Perception For Autonomous Driving – Spring 2017
• ME780 – Nonlinear State Estimation for Robotics and Computer Vision – Spring 2017
• METR 4202/7202 -- Robotics & Automation - University of Queensland
• Robotics - IIT Bombay
• Introduction to Machine Vision
• 6.834J Cognitive Robotics - MIT OCW
• Hello (Real) World with ROS – Robot Operating System - TU Delft
• Programming for Robotics (ROS) - ETH Zurich
• Mechatronic System Design - TU Delft
• CS 206 Evolutionary Robotics Course Spring 2020
• Foundations of Robotics - UTEC 2018-I
• Robotics and Control: Theory and Practice IIT Roorkee
• Mechatronics
• ME142 - Mechatronics Spring 2020 - UC Merced
• Mobile Sensing and Robotics - Bonn University
• MSR2 - Sensors and State Estimation Course (2020) - Bonn University
• SLAM Course (2013) - Bonn University
• ENGR486 Robot Modeling and Control (2014W)
• Robotics by Prof. D K Pratihar - IIT Kharagpur
• Introduction to Mobile Robotics - SS 2019 - Universität Freiburg
• Robot Mapping - WS 2018/19 - Universität Freiburg
• Mechanism and Robot Kinematics - IIT Kharagpur
• Self-Driving Cars - Cyrill Stachniss - Winter 2020/21 - University of Bonn)
• Aerial Robotics - University of Pennsylvania (UPenn)
• Modern Robotics - Northwestern University
• MIT 6.4210/6.4212 - Robotic Manipulation - MIT (Youtube)
• Industrial Robotics and Automation - IIT (ISM) Dhanbad
• MEE5114 Advanced Control for Robotics from Southern University of Science and Technology
• Self-Driving Cars — Andreas Geiger
• Signal Processing: An Introduction by Nathan Kutz
• UC Santa Barbara ME 269 Network Systems, Dynamics and Control fall 2021, by Francesco Bullo
• CMU 16 299 Introduction to Feedback Control Systems spring 2022, by Chris Atkeson
• MAE 509 Linear Matrix Inequality Methods in Optimal and Robust Control, by Matthew M. Peet
• UIUC CS 588 Autonomous Vehicle System Engineering fall 2021, by David Forsyth
• EPFL ME 425 Model Predictive Control fall 2020, by Colin Jones
• Robots That Learn - UC Berkeley CS 294-277

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Computational Finance

• COMP510 - Computational Finance - Steven Skiena - 2007 HKUST
• Computational Finance Course - Prof Grzelak
• Financial Engineering Course: Interest Rates and xVA - Prof Grzelak
• MOOC - Mathematical Methods for Quantitative Finance, University of Washington/Coursera)
• 18.S096 Topics in Mathematics with Applications in Finance, MIT OCW
• Computational Finance - Universität Leipzig
• Machine Learning for Trading | Udacity
• ACT 460 / STA 2502 – Stochastic Methods for Actuarial Science - University of Toronto
• MMF1928H / STA 2503F – Pricing Theory I / Applied Probability for Mathematical Finance - University of Toronto
• STA 4505H – High Frequency & Algorithmic trading - University of Toronto
• Mathematical Finance - IIT Guwahati
• Quantitative Finance - IIT Kanpur
• Financial Derivatives & Risk Management - IIT Roorkee
• Financial Mathematics - IIT Roorkee
• Harvard Economics 2355 Deep Learning for Economics spring 2023, by Melissa Dell
• UW ECON 484 Econometrics and Data Science spring 2020, by Gregory Duncan
• MATH69122 Stochastic Control for Finance
• UC Davis MAT 133 Mathematical Finance Spring 2024, by Matthias Köppe (Spring 2021)

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Blockchain Development

• Blockchain and Cryptocurrencies
  • Blockchain, Solidity, and Full Stack Web3 Development with JavaScript
  • Blockchain Fundamentals Decal 2018 - Berkeley DeCal
  • Blockchain for Developers Decal - Spring 2018 - Berkeley DeCal
  • Cryptocurrency Engineering and Design - Spring 2018 - MIT
  • 15.S12 Blockchain and Money, Fall 2018 - MIT
  • Blockchain - Foundations and Use Cases
• Become Blockchain Developer
  • Solidity for Beginners - Dapp University
  • Master Solidity - Dapp University
  • IPFS Inter Planetary File System Dapp University
  • Solidity, Blockchain, and Smart Contract Course – Beginner to Expert Python Tutorial - FreeCodingCamp
  • Web 3.0 - Build Realtime Decentralized applications

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Misc

• HCI
  • CS147 - Introduction to Human-Computer Interaction Design - Stanford
  • CSEP 510 - Human Computer Interaction
  • Programming for Designers - COMP1400-T2 (2010) - UNSW
  • 08-763 Intro to HCI for Technology Executives - Fall 2015 - CMU
  • 05-600 HCI Pro Seminar - Fall 2015 - CMU
• Game Development
  • CS50's Introduction to Game Development
  • MIT CMS.611J Creating Video Games, Fall 2014
  • MOOC - Beginning Game Programming with C# - Coursera
• Geospatial
  • Introduction to Spatial Data Science, Autumn 2016, University of Chicago
  • Spatial Regression Analysis, Spring 2017, University of Chicago
  • Spatial Data Science, Autumn 2017, University of Chicago
  • Introduction to Geographic Information Systems - IIT Roorkee
• MOOC - Matlab - Coursera
• Computing for Computer Scientists - University of Michigan
• Linux Implementation/Administration Practicum - Redhat by Tulio Llosa
• Innovative Computing - Harvard University
• Linux Programming & Scripting - IIT Madras
• Model Checking - IIT Madras
• Virtual Reality - IIT Madras
• Dependable Systems (SS 2014) - HPI University of Potsdam
• Business Process Compliance (WT 2013/14) - HPI University of Potsdam
• Design Thinking for Digital Engineering (SS 2018) - Dr. Julia von Thienen - HPI
• CS224w – Social Network Analysis – Autumn 2017 - Stanford University
• The Missing Semester of Your CS Education
• 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
• Stanford EE274 I Data Compression: Theory and Applications I 2023
• Probabilistic Methods - University of Waterloo
• Free Probability Theory and Ramanujan Graphs - Spring 2024
• Asymptotics and perturbation methods - Prof. Steven Strogatz
• ETH Zürich AI in the Sciences and Engineering
• Introduction to GIS Programming (Fall 2024) - Open Geospatial Solutions
• UC Berkeley EE 120 Signals and Systems spring 2019, by Murat Arcak
• Stanford EE 102 spring 1999, Introduction to Signals and Systems, by Stephen Boyd
• Stanford EE 376a winter 2011, Information Theory, by Thomas Cover
• MIT RES.6.007 Signals and Systems, 1987 - MIT
• Purdue ME 597 Distributed Energy Resources Spring 2024, by Kevin J. Kircher