Courses:

Introduction to Computer Science

• CS 10 – The Beauty and Joy of Computing – Spring 2015 – Dan Garcia – UC Berkeley InfoCoBuild 209
• 6.0001 – Introduction to Computer Science and Programming in Python – MIT OCW 125
• 6.001 – Structure and Interpretation of Computer Programs, MIT 26
• CS 50 – Introduction to Computer Science, Harvard University 53 (cs50.tv 5)
• CS 61A – Structure and Interpretation of Computer Programs [Python], UC Berkeley 20
• CPSC 110 – Systematic Program Design [Racket], University of British Columbia 9
• CS50’s Understanding Technology 11
• CSE 142 Computer Programming I (Java Programming), Spring 2016 – University of Washington 44
• CS 1301 Intro to computing – Gatech 21
• CS 106A – Programming Methodology, Stanford University 25 (Lecture Videos 2)
• CS 106B – Programming Abstractions, Stanford University 14 (Lecture Videos 2)
• CS 106X – Programming Abstractions in C++ 40 (Lecture Videos 7)
• CS 107 – Programming Paradigms, Stanford University 10
• CmSc 150 – Introduction to Programming with Arcade Games, Simpson College 9
• LINFO 1104 – Paradigms of computer programming, Peter Van Roy, Université catholique de Louvain, Belgium – EdX 11
• FP 101x – Introduction to Functional Programming, TU Delft 14
• Introduction to Problem Solving and Programming – IIT Kanpur 51
• Introduction to programming in C – IIT Kanpur 46
• Programming in C++ – IIT Kharagpur 103
• Python Boot Camp Fall 2016 – Berkeley Institute for Data Science (BIDS) 38
• CS 101 – Introduction to Computer Science – Udacity 26
• 6.00SC – Introduction to Computer Science and Programming (Spring 2011) – MIT OCW 18
• 6.00 – Introduction to Computer Science and Programming (Fall 2008) – MIT OCW 13
• 6.01SC – Introduction to Electrical Engineering and Computer Science I – MIT OCW 12
• Modern C++ Course (2018) – Bonn University 63

Data Structures and Algorithms

• 6.006 – Introduction to Algorithms, MIT OCW 151
• Algorithms: Design and Analysis 1 – Stanford University 110
• Algorithms: Design and Analysis 2 – Stanford University 29
• COS 226 Algorithms, Youtube, Princeton – by Robert Sedgewick and Kevin Wayne 47
• CSE 331 Introduction to Algorithm Design and Analysis, SUNY University at Buffalo, NY – Fall 2017 21 (Lectures 7) (Homework Walkthroughs 2)
• CSE 373 – Analysis of Algorithms, Stony Brook – Prof Skiena 22
• COP 3530 Data Structures and Algorithms, Prof Sahni, UFL 32 (Videos 5)
• CS225 – Data Structures – University of Illinois at Urbana-Champaign 19(Video lectures 7)
• CS2 – Data Structures and Algorithms – Richard Buckland – UNSW 34
• Data Structures – Pepperdine University 15
• CS 161 – Design and Analysis of Algorithms, Prof. Tim Roughgarden, Stanford University 13
• 6.046J – Introduction to Algorithms – Fall 2005, MIT OCW 10
• 6.046 – Design and Analysis of Algorithms, Spring 2015 – MIT OCW 12
• CS 473 – Algorithms – University of Illinois at Urbana-Champaign 13 (Notes – Jeff Erickson 1) (YouTube 4)
• COMP300E – Programming Challenges, Prof Skiena, Hong Kong University of Science and Technology – 2009 14
• 16s-4102 – Algorithms, University of Virginia 11 (Youtube 5)
• CS 170 Algorithms – UCBerkeley 9 Fall 2018, Youtube 5 Fall 2018,Bilibili 1 2013 Bilibili
• ECS 122A – Algorithm Design and Analysis, UC Davis 7
• CSEP 521 – Applied Algorithms, Winter 2013 – University of Washington 7 (Videos 2)
• Data Structures And Algorithms – IIT Delhi 62
• Design and Analysis of Algorithms – IIT Bombay 26
• Programming, Data Structures and Algorithms – IIT Madras 35
• Design and Analysis of Algorithms – IIT Madras 15
• Fundamental Algorithms:Design and Analysis – IIT Kharagpur 7
• Programming and Data Structure – IIT Kharagpur 27
• Programming, Data structures and Algorithms – IIT Madras 28
• Programming, Data Structures and Algorithms in Python – IIT Madras 33
• Programming and Data structures (PDS) – IIT Madras 12
• COP 5536 Advanced Data Structures, Prof Sahni – UFL 5 (Videos 4)
• CS 261 – A Second Course in Algorithms, Stanford University 9 (Youtube 2)
• Informatics 2B – Algorithms, Data Structures and Learning- University of Edinburgh 10
• CS 224 – Advanced Algorithms, Harvard University 7 (Lecture Videos 2) (Youtube 2)
• CS 6150 – Advanced Algorithms (Fall 2016), University of Utah 7
• CS 6150 – Advanced Algorithms (Fall 2017), University of Utah 5
• ECS 222A – Graduate Level Algorithm Design and Analysis, UC Davis 8
• 6.851 – Advanced Data Structures, MIT 18 (MIT OCW)
• 6.854 – Advanced Algorithms, MIT 12 (Prof. Karger lectures 3)
• CS264 Beyond Worst-Case Analysis, Fall 2014 – Tim Roughgarden Lecture 3 (Youtube 2)
• CS364A Algorithmic Game Theory, Fall 2013 – Tim Roughgarden Lectures 4
• CS364B Advanced Mechanism Design, Winter 2014 – Tim Roughgarden Lectures 8
• Algorithms – Aduni 4
• 6.889 – Algorithms for Planar Graphs and Beyond (Fall 2011) MIT 12
• 6.890 Algorithmic Lower Bounds: Fun with Hardness Proofs – MIT OCW 5
• Computer Algorithms – 2 – IIT Kanpur 12
• Parallel Algorithm – IIT Kanpur 16
• Graph Theory – IISC Bangalore 36

Systems Programming

• 6.033 Computer System Engineering – MIT 38
• CS24 Introduction to Computing Systems – California Institute of Technology 18 (Spring 15 version 1)
• 15-213 Introduction to Computer Systems, Fall 2015 – CMU 6
• CS361 – COMPUTER SYSTEMS – UIC 8
• CS 4400 – Computer Systems Fall 2016 – UoUtah 4
• Systems – Aduni 2
• CS110: Principles of Computer Systems – Stanford 7
• Operating Systems
  • CS124 Operating Systems – California Institute of Technology, Fall 2018 – Youtube 68
  • CS 162 Operating Systems and Systems Programming, Spring 2015 – University of California, Berkeley 14
  • CS 4414 – Operating Systems, University of Virginia (rust-class) 12
  • CS 4414 Operating Systems, Fall 2018 – University of Virginia 8
  • CSE 421/521 – Introduction to Operating Systems, SUNY University at Buffalo, NY – Spring 2016 5 (Lectures – YouTube 5) (Recitations 2016 1) (Assignment walkthroughs)
  • CS 377 – Operating Systems, Fall 16 – Umass OS 3
  • 6.828 – Operating System Engineering [Fall 2014] 8
  • CSE 30341 – Operating Systems, Spr 2008 4
  • CSEP 551 Operating Systems Autumn 2014 – University of Washington 7
  • Introduction to Operating Systems – IIT Madras 21
  • CS194 Advanced Operating Systems Structures and Implementation, Spring 2013 InfoCoBuild, UC Berkeley 7
  • CSE 60641 – Graduate Operating Systems, Fall 08 9
• Distributed Systems
  • CS 677 – Distributed Operating Systems, Spring 16 – Umass OS 16
  • CS 436 – Distributed Computer Systems – U Waterloo 12
  • 6.824 – Distributed Systems, Spring 2015 – MIT 17
  • Distributed Algorithms, https://canvas.instructure.com/courses/902299 6
  • CSEP 552 – PMP Distributed Systems, Spring 2013 – University of Washington 8 (Videos)
  • CSE 490H – Scalable Systems: Design, Implementation and Use of Large Scale Clusters, Autumn 2008 – University of Washington 10 (Videos 2)
  • MOOC – Cloud Computing Concepts – UIUC 4
  • Distributed Systems (Prof. Pallab Dasgupta) 11
  • EdX KTHx ID2203 Reliable Distributed Algorithms 2
  • Distributed Data Management – Technische Universität Braunschweig, Germany 3
  • Information Retrieval and Web Search Engines – Technische Universität Braunschweig, Germany 3
  • Middleware and Distributed Systems (WS 2009/10) – Dr. Martin von Löwis – HPI 4
• Real-Time Systems
  • CPCS 663 – Real-Time Systems: Video Material – TAMU 8
  • Real Time Systems – IIT Kharagpur 8
• 6.172 Performance Engineering of Software Systems – MIT OCW 1
• Performance Evaluation of Computer Systems – IIT Madras 1
• Storage Systems – IISC Bangalore 2
• MAP6264 – Queueing Theory – FAU 1(Video Lectures 1)
• EE 380 Colloquim on Computer Systems – Stanford University 5 (Lecture videos 2)

Database Systems

• CMPSC 431W Database Management Systems, Fall 2015 – Penn State University 28 Lectures – YouTube 7
• CS121 – Introduction to Relational Database Systems, Fall 2016 – Caltech 17
• CS 5530 – Database Systems, Spring 2016 – University of Utah 3
• Distributed Data Management (WT 2018/19) – HPI University of Potsdam 2
• MOOC – Database Stanford Dbclass 5
• CSEP 544, Database Management Systems, Au 2015 – University of Washington 4
• Database Design – IIT Madras 7
• Fundamentals of Database Systems – IIT Kanpur 10
• Principles of Database Management, Bart Baesens 1
• FIT9003 Database Systems Design – Monash University 4
• 15-445 – Introduction to Database Systems, CMU 4 (YouTube-2018 2, YouTube-2017)
• 15-721 – Database Systems, CMU 2 (YouTube-2017 3, YouTube-2016)
• 15-721 Advanced Database Systems (Spring 2019) – CMU 2
• CS122 – Relational Database System Implementation, Winter 2014-2015 – Caltech 3
• CS 186 – Database Systems, UC Berkeley, Spring 2015 4 (Lectures- InfoCoBuild 1)
• CS 6530 – Graduate-level Database Systems, Fall 2016, University of Utah 4 (Lectures – YouTube 1)
• 6.830/6.814 – Database Systems [Fall 2014] 4
• Informatics 1 – Data & Analysis 2014/15- University of Edinburgh 4
• Database Management Systems, Aduni 3
• D4M – Signal Processing on Databases
• In-Memory Data Management (2013)Prof. Hasso Plattner – HPI
• Distributed Data Management (WT 2019/20) – Dr. Thorsten Papenbrock – HPI 6

Software Engineering

• Object Oriented Design
  • ECE 462 Object-Oriented Programming using C++ and Java – Purdue 57
  • Object-oriented Program Design and Software Engineering – Aduni 20
  • OOSE – Object-Oriented Software Engineering, Dr. Tim Lethbridge 5
  • Object Oriented Systems Analysis and Design (Systems Analysis and Design in a Changing World) 9
  • CS 251 – Intermediate Software Design (C++ version) – Vanderbilt University 6
  • OOSE – Software Dev Using UML and Java 2
  • Object-Oriented Analysis and Design – IIT Kharagpur 10
  • CS3 – Design in Computing – Richard Buckland UNSW 4
  • Informatics 1 – Object-Oriented Programming 2014/15- University of Edinburgh 3
  • Software Engineering with Objects and Components 2015/16- University of Edinburgh 7
• Software Engineering
  • Computer Science 169- Software Engineering – Spring 2015 – UCBerkeley 33
  • CS 5150 – Software Engineering, Fall 2014 – Cornell University 8
  • Introduction to Service Design and Engineering – University of Trento, Italy 4
  • CS 164 Software Engineering – Harvard 15
  • System Analysis and Design – IISC Bangalore 7
  • Software Engineering – IIT Bombay 16
  • Dependable Systems (SS 2014)- HPI University of Potsdam 3
  • Software Testing – IIT Kharagpur 12
  • Informatics 2C – Software Engineering 2014/15- University of Edinburgh 4
• Software Architecture
  • CS 411 – Software Architecture Design – Bilkent University 20
  • MOOC – Software Architecture & Design – Udacity 14
• Concurrency
  • CS176 – Multiprocessor Synchronization – Brown University 2 (Videos from 2012)
  • CS 282 (2014): Concurrent Java Network Programming in Android 3
  • CSE P 506 – Concurrency, Spring 2011 – University of Washington 1 (Videos)
  • CSEP 524 – Parallel Computation – University of Washington 1 (Videos 1)
  • Parallel Programming Concepts (WT 2013/14) – HPI University of Potsdam 4
  • Parallel Programming Concepts (WT 2012/13) – HPI University of Potsdam 3
• Mobile Application Development
  • MOOC Programming Mobile Applications for Android Handheld Systems – University of Maryland 29
  • CS 193p – Developing Applications for iOS, Stanford University 19
  • CS S-76 Building Mobile Applications – Harvard 22
  • CS 251 (2015): Intermediate Software Design 8
  • Android App Development for Beginners Playlist – thenewboston 28
  • Android Application Development Tutorials – thenewboston 10
  • MOOC – Developing Android Apps – Udacity 17
  • MOOC – Advanced Android App Development – Udacity 16
  • CSSE490 Android Development Rose-Hulman Winter 2010-2011, Dave Fisher 8
  • iOS Course, Dave Fisher 9
  • Developing iPad Applications for Visualization and Insight – Carnegie Mellon University 5
  • Mobile Computing – IIT Madras 15

Artificial Intelligence

• CS 188 – Introduction to Artificial Intelligence, UC Berkeley – Spring 2015 89
• 6.034 Artificial Intelligence, MIT OCW 26
• CS221: Artificial Intelligence: Principles and Techniques – Autumn 2019 – Stanford University 26
• 15-780 – Graduate Artificial Intelligence, Spring 14, CMU 14
• CSE 592 Applications of Artificial Intelligence, Winter 2003 – University of Washington 13
• CS322 – Introduction to Artificial Intelligence, Winter 2012-13 – UBC 10 (YouTube)
• CS 4804: Introduction to Artificial Intelligence, Fall 2016 11
• CS 5804: Introduction to Artificial Intelligence, Spring 2015 2
• Artificial Intelligence – IIT Kharagpur 36
• Artificial Intelligence – IIT Madras 22
• Artificial Intelligence(Prof.P.Dasgupta) – IIT Kharagpur 7
• MOOC – Intro to Artificial Intelligence – Udacity 14
• MOOC – Artificial Intelligence for Robotics – Udacity 11
• Graduate Course in Artificial Intelligence, Autumn 2012 – University of Washington 8
• Agent-Based Systems 2015/16- University of Edinburgh 3
• Informatics 2D – Reasoning and Agents 2014/15- University of Edinburgh 2
• Artificial Intelligence – Hochschule Ravensburg-Weingarten 8
• Deductive Databases and Knowledge-Based Systems – Technische Universität Braunschweig, Germany 7
• Artificial Intelligence: Knowledge Representation and Reasoning – IIT Madras 10
• Semantic Web Technologies by Dr. Harald Sack – HPI 1
• Knowledge Engineering with Semantic Web Technologies by Dr. Harald Sack – HPI 4

Machine Learning

• Introduction to Machine Learning
  • MOOC Machine Learning Andrew Ng – Coursera/Stanford 85 (Notes 25)
  • Introduction to Machine Learning for Coders 31
  • MOOC – Statistical Learning, Stanford University 25
  • Foundations of Machine Learning Boot Camp, Berkeley Simons Institute 12
  • CS155 – Machine Learning & Data Mining, 2017 – Caltech 12 (Notes 2) (2016)
  • CS 156 – Learning from Data, Caltech 4
  • 10-601 – Introduction to Machine Learning (MS) – Tom Mitchell – 2015, CMU 9 (YouTube 3)
  • 10-601 Machine Learning | CMU | Fall 2017 3
  • 10-701 – Introduction to Machine Learning (PhD) – Tom Mitchell, Spring 2011, CMU 8 (Fall 2014) (Spring 2015 by Alex Smola)
  • 10 – 301/601 – Introduction to Machine Learning – Spring 2020 – CMU 10
  • CMS 165 Foundations of Machine Learning and Statistical Inference – 2020 – Caltech 9
  • Microsoft Research – Machine Learning Course 23
  • CS 446 – Machine Learning, Spring 2019, UIUC 3( Fall 2016 Lectures 2)
  • undergraduate machine learning at UBC 2012, Nando de Freitas 3
  • CS 229 – Machine Learning – Stanford University 4 (Autumn 2018)
  • CS 189/289A Introduction to Machine Learning, Prof Jonathan Shewchuk – UCBerkeley 5
  • CPSC 340: Machine Learning and Data Mining (2018) – UBC 4
  • CS4780/5780 Machine Learning, Fall 2013 – Cornell University 3
  • CS4780/5780 Machine Learning, Fall 2018 – Cornell University 2 (Youtube 3)
  • CSE474/574 Introduction to Machine Learning – SUNY University at Buffalo 5
  • CS 5350/6350 – Machine Learning, Fall 2016, University of Utah 5
  • ECE 5984 Introduction to Machine Learning, Spring 2015 – Virginia Tech 4
  • CSx824/ECEx242 Machine Learning, Bert Huang, Fall 2015 – Virginia Tech 1
  • STA 4273H – Large Scale Machine Learning, Winter 2015 – University of Toronto 1
  • CS 485/685 Machine Learning, Shai Ben-David, University of Waterloo
  • STAT 441/841 Classification Winter 2017 , Waterloo 3
  • 10-605 – Machine Learning with Large Datasets, Fall 2016 – CMU 6
  • Information Theory, Pattern Recognition, and Neural Networks – University of Cambridge 1
  • Python and machine learning – Stanford Crowd Course Initiative 9
  • MOOC – Machine Learning Part 1a – Udacity/Georgia Tech 11 (Part 1b 2 Part 2 Part 3)
  • Machine Learning and Pattern Recognition 2015/16- University of Edinburgh
  • Introductory Applied Machine Learning 2015/16- University of Edinburgh 3
  • Pattern Recognition Class (2012)- Universität Heidelberg 2
  • Introduction to Machine Learning and Pattern Recognition – CBCSL OSU 3
  • Introduction to Machine Learning – IIT Kharagpur 13
  • Introduction to Machine Learning – IIT Madras 6
  • Pattern Recognition – IISC Bangalore 5
  • Pattern Recognition and Application – IIT Kharagpur 2
  • Pattern Recognition – IIT Madras 3
  • Machine Learning Summer School 2013 – Max Planck Institute for Intelligent Systems Tübingen 5
  • Machine Learning – Professor Kogan (Spring 2016) – Rutgers 4
  • CS273a: Introduction to Machine Learning 3 (YouTube)
  • Machine Learning Crash Course 2015 2
  • COM4509/COM6509 Machine Learning and Adaptive Intelligence 2015-16 3
  • 10715 Advanced Introduction to Machine Learning 4
  • Introduction to Machine Learning – Spring 2018 – ETH Zurich 3
  • Machine Learning – Pedro Domingos- University of Washington 1
  • Advanced Machine Learning – 2019 – ETH Zürich 9
  • Machine Learning (COMP09012) 4
  • Probabilistic Machine Learning 2020 – University of Tübingen 8
  • Statistical Machine Learning 2020 – Ulrike von Luxburg – University of Tübingen 5
• Data Mining
  • CSEP 546, Data Mining – Pedro Domingos, Sp 2016 – University of Washington 11 (YouTube 3)
  • CS 5140/6140 – Data Mining, Spring 2016, University of Utah 4 (Youtube 4)
  • CS 5955/6955 – Data Mining, University of Utah 3 (YouTube 2)
  • Statistics 202 – Statistical Aspects of Data Mining, Summer 2007 – Google 2 (YouTube 2)
  • MOOC – Text Mining and Analytics by ChengXiang Zhai 2
  • Information Retrieval SS 2014, iTunes – HPI 1
  • MOOC – Data Mining with Weka 2
  • CS 290 DataMining Lectures 5
  • CS246 – Mining Massive Data Sets, Winter 2016, Stanford University 5 (YouTube 2)
  • Data Mining: Learning From Large Datasets – Fall 2017 – ETH Zurich 4
  • Information Retrieval – Spring 2018 – ETH Zurich 1
  • CAP6673 – Data Mining and Machine Learning – FAU 6(Video lectures 3)
  • Data Warehousing and Data Mining Techniques – Technische Universität Braunschweig, Germany 9
• Data Science
  • Data 8: The Foundations of Data Science – UC Berkeley 41 (Summer 17 2)
  • CSE519 – Data Science Fall 2016 – Skiena, SBU 8
  • CS 109 Data Science, Harvard University 13 (YouTube 6)
  • 6.0002 Introduction to Computational Thinking and Data Science – MIT OCW 7
  • Data 100 – Summer 19- UC Berkeley 5
  • Distributed Data Analytics (WT 2017/18) – HPI University of Potsdam 4
  • Statistics 133 – Concepts in Computing with Data, Fall 2013 – UC Berkeley 5
  • Data Profiling and Data Cleansing (WS 2014/15) – HPI University of Potsdam 4
  • AM 207 – Stochastic Methods for Data Analysis, Inference and Optimization, Harvard University 4
  • CS 229r – Algorithms for Big Data, Harvard University 5 (Youtube 1)
  • Algorithms for Big Data – IIT Madras 4
• Probabilistic Graphical Modeling
  • MOOC – Probabilistic Graphical Models – Coursera 5
  • CS 6190 – Probabilistic Modeling, Spring 2016, University of Utah 2
  • 10-708 – Probabilistic Graphical Models, Carnegie Mellon University 2
  • Probabilistic Graphical Models, Daphne Koller, Stanford University 2
  • Probabilistic Models – UNIVERSITY OF HELSINKI 4
  • Probabilistic Modelling and Reasoning 2015/16- University of Edinburgh 2
  • Probabilistic Graphical Models, Spring 2018 – Notre Dame 1
• Deep Learning
  • 6.S191: Introduction to Deep Learning – MIT 30
  • Deep Learning CMU 6
  • Part 1: Practical Deep Learning for Coders, v3 – fast.ai 12
  • Part 2: Deep Learning from the Foundations – fast.ai 6
  • Deep learning at Oxford 2015 – Nando de Freitas 3
  • 6.S094: Deep Learning for Self-Driving Cars – MIT 9
  • CS294-129 Designing, Visualizing and Understanding Deep Neural Networks 2 (YouTube 4)
  • CS230: Deep Learning – Autumn 2018 – Stanford University 6
  • STAT-157 Deep Learning 2019 – UC Berkeley 2
  • Full Stack DL Bootcamp 2019 – UC Berkeley 15
  • Deep Learning, Stanford University 11
  • MOOC – Neural Networks for Machine Learning, Geoffrey Hinton 2016 – Coursera 3
  • Deep Unsupervised Learning – Berkeley Spring 2020 5
  • Stat 946 Deep Learning – University of Waterloo 2
  • Neural networks class – Université de Sherbrooke 1 (YouTube 3)
  • CS294-158 Deep Unsupervised Learning SP19 4
  • DLCV – Deep Learning for Computer Vision – UPC Barcelona 4
  • DLAI – Deep Learning for Artificial Intelligence @ UPC Barcelona 2
  • Neural Networks and Applications – IIT Kharagpur 3
  • UVA DEEP LEARNING COURSE 6
  • Nvidia Machine Learning Class 11
• Reinforcement Learning
  • CS234: Reinforcement Learning – Winter 2019 – Stanford University 7
  • Introduction to reinforcement learning – UCL 2
  • Advanced Deep Learning & Reinforcement Learning – UCL 3
  • Reinforcement Learning – IIT Madras 2
  • CS885 Reinforcement Learning – Spring 2018 – University of Waterloo 2
  • CS 285 – Deep Reinforcement Learning- UC Berkeley
  • CS 294 112 – Reinforcement Learning 1
  • NUS CS 6101 – Deep Reinforcement Learning 2
  • ECE 8851: Reinforcement Learning 1
  • CS294-112, Deep Reinforcement Learning Sp17 1 (YouTube)
  • UCL Course 2015 on Reinforcement Learning by David Silver from DeepMind 1 (YouTube)
  • Deep RL Bootcamp – Berkeley Aug 2017 3
  • Reinforcement Learning – IIT Madras 2
• Advanced Machine Learning
  • Machine Learning 2013 – Nando de Freitas, UBC 9
  • Machine Learning, 2014-2015, University of Oxford 9
  • 10-702/36-702 – Statistical Machine Learning – Larry Wasserman, Spring 2016, CMU 2 (Spring 2015 1)
  • 10-715 Advanced Introduction to Machine Learning – CMU (YouTube 2)
  • CS 281B – Scalable Machine Learning, Alex Smola, UC Berkeley
  • 18.409 Algorithmic Aspects of Machine Learning Spring 2015 – MIT 3
  • CS 330 – Deep Multi-Task and Meta Learning – Fall 2019 – Stanford University 4 (Youtube 2)
• ML based Natural Language Processing and Computer Vision
  • CS 224d – Deep Learning for Natural Language Processing, Stanford University 13 (Lectures – Youtube 4)
  • CS 224N – Natural Language Processing, Stanford University 2 (Lecture videos 2)
  • MOOC – Natural Language Processing, Dan Jurafsky & Chris Manning – Coursera 5
  • fast.ai Code-First Intro to Natural Language Processing 2 (Github)
  • MOOC – Natural Language Processing – Coursera, University of Michigan
  • CS 231n – Convolutional Neural Networks for Visual Recognition, Stanford University
  • CS224U: Natural Language Understanding – Spring 2019 – Stanford University 2
  • Deep Learning for Natural Language Processing, 2017 – Oxford University
  • Machine Learning for Robotics and Computer Vision, WS 2013/2014 – TU München 3 (YouTube 5)
  • Informatics 1 – Cognitive Science 2015/16- University of Edinburgh 1
  • Informatics 2A – Processing Formal and Natural Languages 2016-17 – University of Edinburgh 2
  • Computational Cognitive Science 2015/16- University of Edinburgh 1
  • Accelerated Natural Language Processing 2015/16- University of Edinburgh
  • Natural Language Processing – IIT Bombay 2
  • NOC:Deep Learning For Visual Computing – IIT Kharagpur 5
  • CS 11-747 – Neural Nets for NLP – 2019 – CMU 2
• Time Series Analysis
  • 02417 Time Series Analysis 21
  • Applied Time Series Analysis 12
• Misc Machine Learning Topics
  • EE364a: Convex Optimization I – Stanford University 4
  • CS 6955 – Clustering, Spring 2015, University of Utah 1
  • Info 290 – Analyzing Big Data with Twitter, UC Berkeley school of information 2 (YouTube 2)
  • 10-725 Convex Optimization, Spring 2015 – CMU 1
  • 10-725 Convex Optimization: Fall 2016 – CMU 1
  • CAM 383M – Statistical and Discrete Methods for Scientific Computing, University of Texas 1
  • 9.520 – Statistical Learning Theory and Applications, Fall 2015 – MIT 4
  • Reinforcement Learning – UCL 1
  • Regularization Methods for Machine Learning 2016 (YouTube)
  • Statistical Inference in Big Data – University of Toronto 1
  • 10-725 Optimization Fall 2012 – CMU 2
  • 10-801 Advanced Optimization and Randomized Methods – CMU (YouTube 1)
  • Reinforcement Learning 2015/16- University of Edinburgh 1
  • Reinforcement Learning – IIT Madras
  • Statistical Rethinking Winter 2015 – Richard McElreath 1
  • Music Information Retrieval – University of Victoria, 2014
  • PURDUE Machine Learning Summer School 2011 1
  • Foundations of Machine Learning – Blmmoberg Edu 2
  • Introduction to reinforcement learning – UCL 2
  • Advanced Deep Learning & Reinforcement Learning – UCL 3
  • Web Information Retrieval (Proff. L. Becchetti – A. Vitaletti)
  • Big Data Systems (WT 2019/20) – Prof. Dr. Tilmann Rabl – HPI 3
  • Distributed Data Analytics (WT 2017/18) – Dr. Thorsten Papenbrock – HPI 4

Computer Networks

• 14-740 – Fundamentals of Computer Networks, Fall 2017 – CMU 48
• CS 144 Introduction to Computer Networking – Stanford University, Fall 2013 22 (Lecture videos 11)
• Computer Communication Networks, Rensselaer Polytechnic Institute – Fall 2001 9 (Videos 2) (Slides 1)
• Audio/Video Recordings and Podcasts of Professor Raj Jain’s Lectures – Washington University in St. Louis 4 (YouTube 2)
• Computer Networks, Tanenbaum, Wetherall Computer Networks 5e – Video Lectures 9
• CSEP 561 – PMP Network Systems, Fall 2013 – University of Washington 2 (Videos 1)
• CSEP 561 – Network Systems, Autumn 2008 – University of Washington 2 (Videos 1)
• Computer Networks – IIT Kharagpur 17
• Introduction to Data Communications 2013, Steven Gordon – Thammasat University, Thailand
• Introduction to Complex Networks – RIT 6
• Structural Analysis and Visualization of Networks 2
• Data Communication – IIT Kharagpur 4
• Error Correcting Codes – IISC Bangalore 4
• Information Theory and Coding – IIT Bombay
• Complex Network : Theory and Application – IIT Kharagpur 4
• Advanced 3G and 4G Wireless Mobile Communications – IIT Kanpur 5
• Broadband Networks: Concepts and Technology – IIT Bombay 2
• Coding Theory – IIT Madras 7
• Digital Communication – IIT Bombay
• Digital Voice & Picture Communication – IIT Kharagpur 1
• Wireless Ad Hoc and Sensor Networks – IIT Kharagpur 5
• Internetworking with TCP/IP by Prof. Dr. Christoph Meinel – HPI 7

Math for Computer Scientist

• List of Science & Math courses with video lectures 20
• Calculus
  • 18.01 Single Variable Calculus, Fall 2006 – MIT OCW 16
  • 18.02 Multivariable Calculus, Fall 2007 – MIT OCW 1
  • 18.03 Differential Equations, Spring 2010 – MIT OCW 6
  • Highlights of Calculus – Gilbert Strang, MIT OCW 1
• Discrete Math
  • 6.042J – Mathematics for Computer Science, Fall 2010, MIT OCW 13 (Spring 15 1)
  • Computer Science 70, 001 – Spring 2015 7
  • CSE 547 Discrete Mathematics, Prof Skiena, University of Stony Brook 18
  • Discrete Structures (Summer 2011) – Rutgers, The State University of New Jersey 5
  • Discrete Mathematics and Mathematical Reasoning 2015/16 – University of Edinburgh 12
  • Discrete Mathematical Structures – IIT Madras 15
  • Discrete Structures – Pepperdine University 6
• Probability & Statistics
  • 6.041 Probabilistic Systems Analysis and Applied Probability – MIT OCW 19
  • Statistics 110 – Probability – Harvard University 8
  • STAT 2.1x: Descriptive Statistics | UC Berkeley 3
  • STAT 2.2x: Probability | UC Berkeley 2
  • MOOC – Statistics: Making Sense of Data, Coursera 4
  • MOOC – Statistics One – Coursera 4
  • Probability and Random Processes – IIT Kharagpur 5
  • MOOC – Statistical Inference – Coursera 6
  • 131B – Introduction to Probability and Statistics, UCI 8
  • STATS 250 – Introduction to Statistics and Data Analysis, UMichigan 10
  • Sets, Counting and Probability – Harvard 1
  • Opinionated Lessons in Statistics 2 (Youtube)
  • Statistics – Brandon Foltz 4
  • Statistical Rethinking: A Bayesian Course Using R and Stan 2 (Lectures – Aalto University) (Book)
  • 02402 Introduction to Statistics E12 – Technical University of Denmark 4 (F17)
• Linear Algebra
  • 18.06 – Linear Algebra, Prof. Gilbert Strang, MIT OCW 11
  • Linear Algebra (Princeton University) 4
  • MOOC: Coding the Matrix: Linear Algebra through Computer Science Applications – Coursera 4
  • CS 053 – Coding the Matrix – Brown University 2 (Fall 14 videos)
  • Linear Algebra Review – CMU 2
  • A first course in Linear Algebra – N J Wildberger – UNSW 1
  • INTRODUCTION TO MATRIX ALGEBRA
  • Computational Linear Algebra – fast.ai 1 (Github)
• 10-600 Math Background for ML – CMU 4
• MIT 18.065 Matrix Methods in Data Analysis, Signal Processing, and Machine Learning
• 36-705 – Intermediate Statistics – Larry Wasserman, CMU 1 (YouTube 1)
• Combinatorics – IISC Bangalore 3
• Advanced Engineering Mathematics – Notre Dame 3
• Statistical Computing for Scientists and Engineers – Notre Dame
• Statistical Computing, Fall 2017 – Notre Dame 1

Web Programming and Internet Technologies

• Web Design Decal – HTML/CSS/JavaScript Course, University of California, Berkeley 76
• CS 75 Building Dynamic Websites – Harvard University 30
• Internet Technology – IIT Kharagpur 11
• Introduction to Modern Application Development – IIT Madras 12
• CSE 199 – How the Internet Works, Fall 2016 – University of Buffalo 3
• Open Sourced Elective: Database and Rails – Intro to Ruby on Rails, University of Texas 5 (Lectures – Youtube)
• CSEP545 – Transaction Processing for E-Commerce, Winter 2012 – University of Washington 2 (Videos)
• CT 310 Web Development – Colorado State University 15
• Internet Technologies and Applications 2012, Steven Gordon – Thammasat University, Thailand 4
• CSCI 3110 Advanced Topics in Web Development, Fall 2011 – ETSU iTunes 5
• CSCI 5710 e-Commerce Implementation, Fall 2015 – ETSU iTunes
• MOOC – Web Development – Udacity 32
• Web Technologies Prof. Dr. Christoph Meinel – HPI 5

Theoretical CS and Programming Languages

• MOOC – Compilers – Stanford University 15
• CS 164 Hack your language, UC Berkeley 1 (Lectures – Youtube)
• Theory of computation – Shai Simonson 8
• CS 173 Programming Languages, Brown University 2 (Book)
• CS 421 – Programming Languages and Compilers, UIUC 7
• CSC 253 – CPython internals: A ten-hour codewalk through the Python interpreter source code, University of Rochester 4
• CSE341 – Programming Languages, Dan Grossman, Spring 2013 – University of Washington 3
• CSEP 501 – Compiler Construction, University of Washington 8 (Lectures – Youtube)
• CSEP 505 Programming Languages, Winter 2015 – University of Washington 2
• DMFP – Discrete Mathematics and Functional Programming, Wheaton College 6
• CS 374 – Algorithms & Models of Computation (Fall 2014), UIUC 1 (Lecture videos)
• 6.045 Automata, Computability, and Complexity, MIT (Lecture Videos 1)
• MOOC – Automata – Jeffrey Ullman – Coursera 3
• CS581 Theory of Computation – Portland State University 4 (Lectures – Youtube 3)
• Theory of Computation – Fall 2011 UC Davis 4
• TDA555 Introduction to Functional Programming – Chalmers University of Technology 1 (Lectures – YouTube)
• Philip Wadler Haskell lecture recordings 1
• Functional Programming – University of Edinburgh – 2016-17 1
• MOOC – Functional Programming Principles in Scala by Martin Odersky 5
• CS294 – Program Synthesis for Everyone
• MOOC – Principles of Reactive Programming, Scala – Coursera 3
• Category Theory for Programmers, 2014 – Bartosz Milewski (YouTube)
• Oregon Programming Languages Summer School (Proof theory, type theory, category theory, verification)
  • 2012 Lectures 1
  • 2013 Lectures
  • 2014 Lectures 1
  • 2015 Lectures
  • 2016 Lectures 3
• Inf1 – Computation and Logic 2015 – University of Edinburgh
• INFORMATICS 1 – FUNCTIONAL PROGRAMMING – University of Edinburgh 1 (Videos 1)
• Compiler Design – IISC Bangalore 6
• Compiler Design – IIT Kanpur 10
• Principles of Programming Languages – IIT Delhi 3
• Principles of Compiler Design – IISC Bangalore 6
• Functional Programming in Haskell – IIT Madras
• Theory of Computation – IIT Kanpur 4
• Theory of Automata, Formal Languages and Computation – IIT Madras 5
• Theory of Computation – IIT Kanpur 5
• Logic for CS – IIT Delhi 2
• Principles of Compiler Design – Swarthmore College 6
• Undergrad Complexity Theory at CMU
• Graduate Complexity Theory at CMU
• Great Ideas in Theoretical Computer Science at CMU
• Analysis of Boolean Functions at CMU
• Theoretical Computer Science (Bridging Course)(Tutorial) – SS 2015

Embedded Systems

• EE319K Embedded Systems – UT Austin 30
• EE445L Embedded Systems Design Lab, Fall 2015, UTexas 9
• CS149 Embedded Systems – Fall 2014 – UCBerkeley 5
• ECE 4760 Designing with Microcontrollers Fall 2016, Cornell University 7 (Lectures – Youtube 1)
• ECE 5760 – Advanced Microcontroller Design and system-on-chip, Spring 2016 – Cornell University 2
• CSE 438/598 Embedded Systems Programming ASU 7
• Summer Short Course on Embedded Systems Programming 10
• Internet of Things by Prof. Dr.-Ing. Dietmar P. F. Möller 10
• CSE 351 – The Hardware/Software Interface, Spring 16 – University of Washington 4 (Coursera)
• ECE 5030 – Electronic Bioinstrumentation, Spring 2014 – Cornell University 3
• ECE/CS 5780/6780 – Embedded Systems Design, Spring 14 – University of Utah 4
• Embedded Systems Class – Version 1 – 2011 – UNCC 4
• Embedded Systems using the Renesas RX63N Processor – Version 3 – UNCC 5
• ELEC2142 – Embedded Systems Design – UNSW 4
• Software Engineering for Embedded Systems (WS 2011/12) – HPI Univesrity of Potsdam 5
• Embedded Software Testing – IIT Madras 5
• Embedded Systems – IIT Delhi 6
• Embedded Systems Design – IIT Kharagpur 4
• ARM Based Development – IIT Madras 6
• Software Engineering for Self Adaptive Systems – iTunes – HPI Univesrity of Potsdam 2
• EE260 Embedded Systems by Robert Paz 4
• IoT Summer School 5
• ECSE 421 – Embedded Systems – McGill 3
• EE402 – Object-oriented Programming with Embedded Systems 7
• NOC:Advanced IOT Applications – IISc Bangalore 7
• NOC:Design for internet of things – IISc Bangalore 4

Computer Organization and Architecture

• Computer Organization
  • How Computers Work – Aduni 12
  • CS 61C – Machine Structures, UC Berkeley 6 (Lectures – InfoCoBuild 2)
  • 6.004 – Computation Structures Spring 2013, MIT 2
  • CS/ECE 3810 Computer Organization, Fall 2015, , University of Utah 2 (YouTube)
  • Digital Computer Organization – IIT Kharagpur 4
  • Computer Organization – IIT Madras 12
  • CS-224 – Computer Organization, 2009-2010 Spring, Bilkent University 2 (YouTube playlist)
  • INFORMATICS 2C – INTRODUCTION TO COMPUTER SYSTEMS (AUTUMN 2016) – University of Edinburgh 2
• Computer Architecture
  • 18-447 – Introduction to Computer Architecture, CMU 11 (Lectures – YouTube – Fall 15)
  • CS 152 Computer Architecture and Engineering, UC Berkeley 6
  • CSEP 548 – Computer Architecture Autumn 2012 – University of Washington 4
  • CS/ECE 6810 Computer Architecture, Spring 2016, University of Utah 5 (YouTube 1)
  • MOOC – Computer Architecture, David Wentzlaff – Princeton University/Coursera 6
  • Computer Architecture – ETH Zürich – Fall 2019 4
  • Digital Circuits and Computer Architecture – ETH Zurich – Spring 2017 2
  • Computer Architecture – IIT Delhi 6
  • Computer Architecture – IIT Kanpur
  • Computer Architecture – IIT Madras
  • High Performance Computer Architecture – IIT Kharagpur 3
• Parallel Computer Architecture
  • 15-418 – Parallel Computer Architecture and Programming, CMU 4 (Lecture Videos 1)
  • CS 267 Applications of Parallel Computers, Spring 16 – UC Berkeley 1 (YouTube 2)
  • MOOC – Heterogeneous Parallel Programming – Coursera 1
  • ECE 498AL – Programming Massively Parallel Processors 3
  • Parallel Computing – IIT Delhi 2
  • Parallel Architectures 2012/13- University of Edinburgh 3
• Digital Systems Design
  • ELEC2141 Digital Circuit Design, UNSW 7
  • Digital Systems Design – IIT Kharagpur 7
  • Digital Design Course – 2015 – UNCC 5
• CS1 – Higher Computing – Richard Buckland UNSW 2
• MOOC – From NAND to TetrisBuilding a Modern Computer From First Principles (YouTube)
• System Validation, TU Delft
• High Performance Computing – IISC Bangalore 6
• Introduction to ARM – Open SecurityTraining 2
• Intro x86 (32 bit) – Open SecurityTraining 1
• Intermediate x86 (32 bit) – Open SecurityTraining
• Design of Digital Circuits – ETH Zürich – Spring 2019 2
• Onur Mutlu @ TU Wien 2019 – Memory Systems 1
• Memory Systems Course – Technion, Summer 2018 3

Security

• Internet Security (WT 2018/19) – HPI University of Potsdam 19
• 6.858 Computer Systems Security – MIT OCW 3
• 6.875 – Cryptography – Spring 2018- MIT 8
• CSEP590A – Practical Aspects of Modern Cryptography, Winter 2011 – University of Washington 2 (Videos 1)
• CS461/ECE422 – Computer Security – University of Illinois at Urbana-Champaign 6 (Videos)
• Introduction to Cryptography, Christof Paar – Ruhr University Bochum, Germany 3
• ECS235B Foundations of Computer and Information Security – UC Davis 1
• CIS 4930/ CIS 5930 – Offensive Computer Security, Florida State University 3
• Introduction to Information Security I – IIT Madras 10
• Information Security – II – IIT Madras 7
• Introduction to Cryptology – IIT Roorkee 5
• Cryptography and Network Security – IIT Kharagpur 8
• 18-636 Browser Security, Stanford 2
• Internet Security – Weaknesses and Targets (WT 2015/16) 2 (WT 2012/13 (YouTube))
• IT Security, Steven Gordon – Thammasat University, Thailand 3
• Security and Cryptography, Steven Gordon – Thammasat University, Thailand 3
• MOOC – Cryptography – Coursera 6
• MOOC – Intro to Information Security – Udacity 3
• ICS 444 – Computer & Network Security 3
• Privacy and Security in Online Social Networks – IIT Madras
• Malware Dynamic Analysis – Open SecurityTraining 2 (YouTube)
• CSN09112 – Network Security and Cryptography – Bill Buchanan – Edinburgh Napier 7
• CSN10107 – Security Testing and Network Forensics – Bill Buchanan – Edinburgh Napier 3
• CSN11123 – Advanced Cloud and Network Forensics – Bill Buchanan – Edinburgh Napier
• CSN11117 – e-Security – Bill Buchanan – Edinburgh Napier 2
• CSN08704 – Telecommunications – Bill Buchanan – Edinburgh Napier
• CSN11128 – Incident Response and Malware Analysus – Bill Buchanan – Edinburgh Napier 3
• Internet Security for Beginners by Dr. Christoph Meinel – HPI 2

Computer Graphics

• CS184 – Computer Graphics, Fall 2012 – UC Berkeley 19
• ECS 175 – Computer Graphics, Fall 2009 – UC Davis 3
• 6.837 – Computer Graphics – Spring 2017 – MIT 6
• 6.838 – Shape Analysis – Spring 2017- MIT 1
• Introduction to Computer Graphics – IIT Delhi 6
• Computer Graphics – IIT Madras 8
• Computer Graphics 2012, Wolfgang Huerst, Utrecht University
• CS 5630/6630 – Visualization, Fall 2016, University of Utah (Lectures – Youtube)
• Advanced Visualization UC Davis
• CSCI E-234 – Introduction to Computer Graphics and GPU Programming, Harvard Extension School 3
• Computer Graphics Fall 2011, Barbara Hecker 4
• Introduction to Graphics Architecture 3
• Ray Tracing for Global Illumination, UCDavis 1
• Rendering / Ray Tracing Course, SS 2015 – TU Wien
• ECS 178 Introduction to Geometric Modeling, Fall 2012, UC Davis 1 (iTunes)
• Computational Geometry – IIT Delhi 3
• CS 468 – Differential Geometry for Computer Science – Stanford University 1 (Lecture videos 1)

Image Processing and Computer Vision

• MOOC – Digital Image procesing – Duke/Coursera 18
• Computer Vision 2011 – EPFL, Switzerland 5
• Digital Image Processing – IIT Kharagpur 9
• Image Processing and Analysis – UC Davis 2
• CS 543 – Computer Vision – Spring 2017 2 (Recordings 2)
• CAP 5415 – Computer Vision – University of Central Florida 1(Video Lectures)
• EE225B – Digital Image Processing, Spring 2014 – UC Berkeley 2 (Videos – Spring 2006 1)
• EE637 – Digital Image Processing I – Purdue University 2 (Videos – Sp 2011 2,Videos – Sp 2007)
• Computer Vision I: Variational Methods – TU München 4 (YouTube)
• Computer Vision II: Multiple View Geometry (IN2228), SS 2016 – TU München 1 (YouTube)
• EGGN 510 – Image and Multidimensional Signal Processing – Colorado School of Mines 1
• EENG 512/CSCI 512 – Computer Vision – Colorado School of Mines 2
• Computer Vision for Visual Effects – RPI 2 (YouTube)
• Introduction to Image Processing – RPI 2 (YouTube 2)
• CAP 6412 – Advanced Computer Vision – University of Central Florida 1(Video lectures 1) (Spring 2018)
• Digital Signal Processing – RPI
• Advanced Vision 2014 – University of Edinburgh
• Photogrammetry Course – 2015/16 – University of Bonn, Germany 3
• MOOC – Introduction to Computer Vision – Udacity 5
• ECSE-4540 – Intro to Digital Image Processing – Spring 2015 – RPI 2
• Machine Learning for Computer Vision – Winter 2017-2018 – UniHeidelberg 3
• High-Level Vision – CBCSL OSU
• Advanced Computer Vision – CBCSL OSU 1
• Introduction to Image Processing & Computer Vision – CBCSL OSU 1
• Machine Learning for Computer Vision – TU Munich 5
• Biometrics – IIT Kanpur 2
• Quantitative Big Imaging 2019 ETH Zurich 1
• Multiple View Geometry in Computer Vision 2

Computational Biology

• ECS 124 – Foundations of Algorithms for Bioinformatics – Dan Gusfield, UC Davis 8 (YouTube 1)
• CSE549 – Computational Biology – Steven Skiena – 2010 SBU 5
• 7.32 Systems Biology, Fall 2014 – MIT OCW 3
• 6.802J/ 6.874J Foundations of Computational and Systems Biology – MIT OCW 2
• 6.047/6.878 Public Lectures on Computational Biology: Genomes, Networks, Evolution – MIT 1
• Bio 84 – Your Genes and Your Health, Stanford University
• BioMedical Informatics 231 Computational Molecular Biology, Stanford University 5
• BioMedical Informatics 258 Genomics, Bioinformatics & Medicine, Stanford University 4
• 03-251: Introduction to Computational Molecular Biology – Carnegie Mellon University 4
• 03-712: Biological Modeling and Simulation – Carnegie Mellon University 2
• MOOC – Bioinformatics Algorithms: An Active Learning Approach – UC San Diego/Coursera 3
• Neural Networks and Biological Modeling – Lecturer: Prof. Wulfram Gerstner – EPFL 2
• Video Lectures of Wulfram Gerstner: Computational Neuroscience – EPFL 1
• An Introduction To Systems Biology
• Introduction to Bioinformatics, METUOpenCourseWare 5
• MOOC – Algorithms for DNA Sequencing, Coursera 3
• Frontiers of Biomedical Engineering with W. Mark Saltzman – Yale 1
• NOC:Computational Systems Biology – IIT Madras 1
• NOC:BioInformatics:Algorithms and Applications – IIT Madras 3

Quantum Computing

• 15-859BB: Quantum Computation and Quantum Information 2018 – CMU 12 (Youtube 2)
• Quantum Mechanics for Scientists and Engineers 5
• Quantum Mechanics and Quantum Computation – Umesh Vazirani 5
• Quantum Information and Computing by Prof. D.K. Ghosh 5
• Quantum Computing by Prof. Debabrata Goswami 4
• The Building Blocks of a Quantum Computer: Part 1 – TU Delft 7
• The Building Blocks of a Quantum Computer: Part 2 – TU Delft 3
• Quantum Cryptography – TU Delft 5

Robotics

• CS 223A – Introduction to Robotics, Stanford University 33
• 6.832 Underactuated Robotics – MIT OCW 7
• CS287 Advanced Robotics at UC Berkeley Fall 2019 – Instructor: Pieter Abbeel 7
• CS 287 – Advanced Robotics, Fall 2011, UC Berkeley 1 (Videos 1)
• CS235 – Applied Robot Design for Non-Robot-Designers – Stanford University 4
• Lecture: Visual Navigation for Flying Robots 3 (YouTube)
• CS 205A: Mathematical Methods for Robotics, Vision, and Graphics (Fall 2013) 5
• Robotics 1, Prof. De Luca, Università di Roma 4 (YouTube)
• Robotics 2, Prof. De Luca, Università di Roma 1 (YouTube)
• Robot Mechanics and Control, SNU 3
• Introduction to Robotics Course – UNCC 3
• SLAM Lectures 2
• Introduction to Vision and Robotics 2015/16- University of Edinburgh 1
• ME 597 – Autonomous Mobile Robotics – Fall 2014 2
• ME 780 – Perception For Autonomous Driving – Spring 2017 4
• ME780 – Nonlinear State Estimation for Robotics and Computer Vision – Spring 2017 1
• METR 4202/7202 – Robotics & Automation – University of Queensland 7
• Robotics – IIT Bombay 9
• Introduction to Machine Vision
• 6.834J Cognitive Robotics – MIT OCW 3
• Hello (Real) World with ROS – Robot Operating System – TU Delft 5
• Programming for Robotics (ROS) – ETH Zurich 6
• Mechatronic System Design – TU Delft 7
• CS 206 Evolutionary Robotics Course Spring 2020 2
• Foundations of Robotics – UTEC 2018-I 2
• Robotics – Youtube 4
• Robotics and Control: Theory and Practice IIT Roorkee 2
• Mechatronics 1
• ME142 – Mechatronics Spring 2020 – UC Merced
• Mobile Sensing and Robotics – Bonn University
• MSR2 – Sensors and State Estimation Course (2020) – Bonn University 1
• SLAM Course (2013) – Bonn University 1
• ENGR486 Robot Modeling and Control (2014W)
• Robotics by Prof. D K Pratihar – IIT Kharagpur 4
• Introduction to Mobile Robotics – SS 2019 – Universität Freiburg 5
• Robot Mapping – WS 2018/19 – Universität Freiburg 5
• Mechanism and Robot Kinematics – IIT Kharagpur 3

Computational Finance

• COMP510 – Computational Finance – Steven Skiena – 2007 HKUST 13
• MOOC – Mathematical Methods for Quantitative Finance, University of Washington/Coursera) 6
• 18.S096 Topics in Mathematics with Applications in Finance, MIT OCW 4
• Computational Finance – Universität Leipzig 1
• Machine Learning for Trading | Udacity 15
• ACT 460 / STA 2502 – Stochastic Methods for Actuarial Science – University of Toronto 2
• MMF1928H / STA 2503F – Pricing Theory I / Applied Probability for Mathematical Finance – University of Toronto 3
• STA 4505H – High Frequency & Algorithmic trading – University of Toronto 14
• Mathematical Finance – IIT Guwahati 7
• Quantitative Finance – IIT Kanpur 8
• Financial Derivatives & Risk Management – IIT Roorkee 12
• Financial Mathematics – IIT Roorkee 8

Misc

• HCI
  • CS147 – Introduction to Human-Computer Interaction Design – Stanford 6
  • CSEP 510 – Human Computer Interaction 3
  • Programming for Designers – COMP1400-T2 (2010) – UNSW 1
  • 08-763 Intro to HCI for Technology Executives – Fall 2015 – CMU 3
  • 05-600 HCI Pro Seminar – Fall 2015 – CMU
• Game Development
  • MIT CMS.611J Creating Video Games, Fall 2014 23
  • MOOC – Beginning Game Programming with C# – Coursera 23
• Geospatial
  • Introduction to Spatial Data Science, Autumn 2016, University of Chicago 4
  • Spatial Regression Analysis, Spring 2017, University of Chicago 2
  • Spatial Data Science, Autumn 2017, University of Chicago 2
  • Introduction to Geographic Information Systems – IIT Roorkee 1
• SCICOMP – An Introduction to Efficient Scientific Computation, Universität Bremen 2
• CS E-259 XML with Java, Java Servlet, and JSP – Harvard 8
• CSE 40373 – Spr 2009: Multimedia Systems
• Exposing Digital Photography – Harvard Extension School 5
• MOOC – Matlab – Coursera 11
• Computing for Computer Scientists – University of Michigan 4
• Linux Implementation/Administration Practicum – Redhat by Tulio Llosa 6
• SIMS 141 – Search Engines – Fall 2005 UCBerkeley
• Innovative Computing – Harvard University
• Linux Programming & Scripting – IIT Madras 22
• Model Checking – IIT Madras 2
• Virtual Reality – IIT Madras 12
• CS 195 – Social Implications of Computing, Spring 2015 – UC Berkeley 2 (YouTube)
• Spatial Databases and Geographic Information Systems – Technische Universität Braunschweig, Germany (in German) 3
• Dependable Systems (SS 2014) – HPI University of Potsdam 3
• Business Process Compliance (WT 2013/14) – HPI University of Potsdam 2
• Design Thinking for Digital Engineering (SS 2018) – Dr. Julia von Thienen – HPI 6
• CS224w – Social Network Analysis – Autumn 2017 – Stanford University 5
• Blockchain and Cryptocurrencies
  • Blockchain Fundamentals Decal 2018 – Berkeley DeCal 49
  • Blockchain for Developers Decal – Spring 2018 – Berkeley DeCal 28
  • Cryptocurrency Engineering and Design – Spring 2018 – MIT 22
  • 15.S12 Blockchain and Money, Fall 2018- MIT 22

Source : Github