Before starting, I would recommend watching these two awesome videos by Andrew Ng on general career advice. [short | long]
One should start from probability, linear algebra, optimization, then to go deep into deep learning related topics. Here are the links:
| Mathematical topics | Link | Professor (Institution) |
|---|---|---|
| Linear Algebra | Lectures | Gilbert Strang (MIT) |
| Probability | Lectures | John Tsitsiklis (MIT) |
| MIT RES.LL-005 Mathematics of Big Data and Machine Learning | Lectures | Jeremy Kepner, Vijay Gadepally (MIT) |
| Probability for Computer Scientists | Lectures | Jerry Cain (Stanford University) |
| Probability | Lectures | Joe Blitzstein (Harvard) |
| Optimization related to deep learning | Lectures | Gilbert Strang (MIT) |
| Optimization (General) | Lectures | Geoff Gordon (CMU) |
| Convex optimization | Lectures | Ryan Tibshirani (CMU) |
| Causal Inference | Lectures | Brady Neal (MILA) |
| Probabilistic Graphic Models | Lectures | Kayhan Batmaghelich (Now at Boston University, lectures recorded at CMU) |
| Probablistic Graphical Models | Lectures | Daphne koller (Stanford) |
| Engineering Math: Differential Equations and Dynamical Systems | Lectures | Steve Brunton (University of Washington) |
| Engineering Math: Vector Calculus and Partial Differential Equations | Lectures | Steve Brunton (University of Washington) |
| Complex Analysis | Lectures | Steve Brunton (University of Washington) |
| Professor (Institute) | Course name (Link) | Course id |
|---|---|---|
| Bhiksha Raj (CMU) | Introduction to Deep Learning | 11-785 |
| Soheil Feizi (UMD) | Foundations of Deep Learning | CMSC 828W |
| Yann LeCun, Alfredo Canziani (NYU) | DEEP LEARNING | DS-GA 1008 |
| Andrew Ng (Stanford) | Deep Learning | CS 230 |
| Alexander Amini (MIT) | Introduction to Deep Learning | MIT 6.S191 |
| Sergey Levine (UC Berkeley) | Deep Learning | CS W182 / 282A |
| Pieter Abbeel (UC Berkeley) | Deep Unsupervised Learning | CS294-158 |
| Andreas Geiger (University of Tübingen) | Computer vision | Course link |
| Louis-Philippe Morency (CMU) | Multimodal Machine Learning | 11-777 |
| Jake Austin, Arvind Rajaraman, Aryan Jain, Rohan Viswanathan, Ryan Alameddine, & Verona Teo (UC Berkeley) | Modern Computer Vision | CS 198-126 |
| Yogesh S Rawat (UCF) | Computer Vision | CAP5415 |
| Mubarak Shah (UCF) | Advanced Computer Vision | CAP6412 |
| Chen Chen (UCF) | Medical Image Computing | CAP 5516 |
| Justin Johnson (University of Michigan) | Deep Learning for Computer Vision | EECS 498.008 / 598.008 |
| Christopher Manning (Standford) | NLP with Deep Learning | CS224N |
| Graham Neubig (CMU) | Advanced NLP | CMU CS 11-711 |
| Sergey Levine (UC Berkeley) | Deep Reinforcement Learning | CS285 |
| Emma Brunskill (Stanford) | Reinforcement Learning | CS234 |
| Chelsea Finn (Stanford) | Deep Multi-Task & Meta Learning | CS330 |
| DeepFindr - Talk series | Understanding Graph Neural Networks | |
| Nando de Freitas (University of Oxford) | Deep Learning | Course Link |
| Ali Ghodsi (University of Waterloo) | Deep Learning | STAT 940 |
| Ali Ghodsi (University of Waterloo) | Deep Learning | STAT 946 |
| Ali Ghodsi (University of Waterloo) | Data Visualization | STAT 442/842 |
| Tianqi Chen, Zico Kolter (CMU) | Deep Learning Systems | 10-414/714 |
| Steve Brunton (University of Washington) | Data-Driven Dynamical Systems | Course Link |
| Volodymyr Kuleshov (Cornell University) | Deep Generative Models | CS 6785 |
| Professor (Institute) | Course name (Link) | Course id |
|---|---|---|
| Yaser Abu-Mostafa (Caltech) | Machine Learning | CS-156 |
| Andrew Ng (Stanford) | Machine Learning | CS 229 |
| Roni Rosenfel (CMU) | Machine Learning | 10601A / 10601C |
| Anima Anadkumar (Caltech) | Foundations of Machine Learning and Statistical Inference | CS 165 |
| Von Luxburg and Hennig(University of Tübingen) | Probabilistic and Statistical Machine Learning | Course |
| Patrick Winston (MIT) | Artificial Intelligence | 6.034 |
| Himabindu (Hima) Lakkaraju (Harvard) | Machine Learning Explainability Workshop | Exaplainable workshop at Stanford |
| Cynthia Rudin (Duke University) | Intuition for the Algorithms of Machine Learning | |
| Jure Leskovec (Stanford) | Machine Learning with Graphs | CS 224W |
| David Sontag (MIT) | Machine Learning for Healthcare | 6.S897 |
| Manolis Kellis, James Galagan (MIT) | Machine Learning in Genomics | 6.047/6.878 |
| Percy Liang, Dorsa Sadigh (Stanford) | Artificial Intelligence | CS 221 |
| Nando de Freitas (UBC) | Machine Learning | CS 540 |
| Nando de Freitas (UBC) | Machine Learning Undergrad | CS 340 |
| Mohamed Abdelfattah (Cornell University) | Machine Learning Hardware & Systems | ECE 5545 (CS 5775) |
| Kilian Weinberger (Cornell University) | Machine Learning for Intelligent Systems | CS 4780/CS 5780 |
| Steve Brunton (University of Washington) | Physics Informed Machine Learning | |
| Andreas Mueller (Columbia University) | Applied Machine Learning | COMS W4995 |
| Volodymyr Kuleshov (Cornell University) | Applied Machine Learning | Cornell CS 5787 |
| Chip Huyen (Stanford University) | Machine Learning Systems Design | CS 329S |
| Professor (Institute) | Course name (Link) | Course id |
|---|---|---|
| Percy Liang (Stanford) | Large Language Models | CS324 |
| Div Garg (Stanford) | Transformers | CS25 |
| Lei Li (CMU) | Large Language Model Systems | 11868 |
| Chenyan Xiong (CMU) | Large Language Models Methods and Applications | 11667 |
| Dawn Song (UC Berkeley) | Understanding Large Language Models: Foundations and Safety | CS194/294-267 |
| Wenhu Chen (University of Waterloo) | CS 886: Recent Advances on Foundation Models | CS 886 |
Have anything in mind that you think is awesome and would fit in this list? Feel free to send a pull request.