List of links which are not working.

[ShreJais]

Please add the new working link.

Robotics and Control:

1. Introduction to Vision and Robotics 2015/16- University of Edinburgh
2. Robotics - Youtube
3. Mobile Sensing and Robotics 1 – Part Stachniss (Jointly taught with PhoRS) - University of Bonn
4. Mobile Sensing and Robotics 2 – Stachniss & Klingbeil/Holst - University of Bonn
5. EPFL EE 611 Linear System Theory spring 2020, by Philippe Müllhaupt
6. EPFL ME 427 Networked Control Systems spring 2020, by Giancarlo Ferrari Trecate
7. EPFL ME 422 Multivariable Control spring 2020, by Giancarlo Ferrari Trecate
8. UW EE 549 State Estimation and Kalman Filtering spring 2009, by Kristi Morgansen

[ShreJais]

Misc:

1. SCICOMP - An Introduction to Efficient Scientific Computation, Universität Bremen
2. CS E-259 XML with Java, Java Servlet, and JSP - Harvard
3. CSE 40373 - Spr 2009: Multimedia Systems
4. Exposing Digital Photography - Harvard Extension School
5. SIMS 141 - Search Engines - Fall 2005 UCBerkeley
6. CS 195 - Social Implications of Computing, Spring 2015 - UC Berkeley (YouTube)
7. Spatial Databases and Geographic Information Systems - Technische Universität Braunschweig, Germany (in German)
8. LINGI 2365 Constraint Programming 2021, by Pierre Schaus - UCLouvain

[ShreJais]

Image Processing and Computer Vision:

1. MOOC - Digital Image processing - Duke/Coursera
2. Image Processing and Analysis - UC Davis
3. EGGN 510 - Image and Multidimensional Signal Processing - Colorado School of Mines
4. 3D Coordinate Systems – Remote Course (GE, 2020) - University of Bonn
5. (2013 lectures)

Computer Graphics:

1. CSCI E-234 - Introduction to Computer Graphics and GPU Programming, Harvard Extension School
2. Introduction to Graphics Architecture
3. ECS 178 Introduction to Geometric Modeling, Fall 2012, UC Davis (iTunes)
4. Columbia COMS W4195 Computational Techniques in Pixel Processing fall 2004, by George Wolberg

[ShreJais]

Machine Learning:

A. Introduction to ML:

1. 10 - 301/601 - Introduction to Machine Learning - Spring 2020 - CMU
2. Machine Learning and Pattern Recognition 2015/16- University of Edinburgh
3. Introductory Applied Machine Learning 2015/16- University of Edinburgh
4. Advanced Machine Learning - 2019 - ETH Zürich
5. 10715 Advanced Introduction to Machine Learning
6. CS189 Machine Learning 2022 - UCB
7. ETH Zurich Statistical Learning Theory spring 2021, by Joachim M. Buhmann
8. UC Berkeley CS 189 / 289A Introduction to Machine Learning spring 2022, by Jonathan Shewchuk
9. UT Austin Machine Learning Algorithms & Statistical Learning by Adam Klivans & Qiang Liu
10. EPFL CS 233 Introduction to Machine Learning fall 2022, by Mathieu Salzmann
11. Statistics 133 - Concepts in Computing with Data, Fall 2013 - UC Berkeley B. Data Mining:
12. Data Mining: Learning From Large Datasets - Fall 2017 - ETH Zurich
13. Data Warehousing and Data Mining Techniques - Technische Universität Braunschweig, Germany

C. Probabilistic Graphical Modelling

1. MOOC - Probabilistic Graphical Models - Coursera
2. Probabilistic Models - UNIVERSITY OF HELSINKI
3. Probabilistic Modelling and Reasoning 2015/16- University of Edinburgh

D. Deep Learning:

1. Nvidia Machine Learning Class
2. CMU 10 707 Deep Learning fall 2017 by Ruslan Salakhutdinov
3. UT Austin CS 394D Deep Learning fall 2021, by Philipp KrahenBühl
4. CMU 10 417 / 10 617 Intermediate Deep Learning fall 2022, by Ruslan Salakhutdinov
5. STATS 385 Analysis of Deep Learning - Stanford
6. STATS 385 Theories of Deep Learning - Stanford

E. Reinforcement Learning

1. Advanced Deep Learning & Reinforcement Learning - UCL

F. NLP:

1. Accelerated Natural Language Processing 2015/16- University of Edinburgh

G. Computer Vision:

1. Computational Cognitive Science 2015/16- University of Edinburgh

H. Misc ML Topics:

1. CAM 383M - Statistical and Discrete Methods for Scientific Computing, University of Texas
2. Reinforcement Learning 2015/16- University of Edinburgh
3. Advanced Deep Learning & Reinforcement Learning - UCL
4. PURDUE Machine Learning Summer School 2011
5. Music Information Retrieval - University of Victoria, 2014
6. UC Irvine CS 274B Learning in Graphical Models spring 2021, by Erik Sudderth
7. EPFL COM 516 Markov Chains and Algorithmic Applications spring 2020, by Olivier Leveque

[Developer-Y]

Thanks for preparing the list, these links have been removed now.