[D] Advanced courses update

We have a PhD level or Advanced courses thread in the sidebar but it's three year old now. There were two other 7-8 month old threads (1, 2) but they don't have many quality responses either.

So, can we have a new one here?

To reiterate - CS231n, CS229, ones from Udemy etc are not advanced.

Advanced ML/DL/RL, attempts at building theory of DL, optimization theory, advanced applications etc are some examples of what I believe should belong here, much like the original sidebar post.

You can also suggest (new) categories for the courses you share. :)

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Here are some courses we've found so far.

ML >>

• Learning Discrete Latent Structure - sta4273/csc2547 Spring'18
• Learning to Search - csc2547 Fall'19
• Scalable and Flexible Models of Uncertainty - csc2541
• Meta-Learning - ICML 2019 Tutorial , Metalearning: Applications to Data Mining - google books link
• Fundamentals of Machine Learning Over Networks - ep3260
• Topics in Deployable ML - 6.S979
• Machine Learning on Graphs - cs224w
• Probabilistic Graphical Methods - 10-708
• Mining Massive Data Sets - cs246
• Bayesian Data Analysis
• Advanced Machine Learning Systems - cs6787 - lecture 9 and onwards discuss hardware side of things
• Visual Computing Systems- cs348v - Another systems course that discusses hardware from a persepective of visual computing but is relevant to ML as well
• Machine Learning for Sequential Decision Making Under Uncertainty - ee290s/cs194
• Interactive Learning - cse599

ML >> Theory

• Statistical Machine Learning - 10-702/36-702 with videos, 2016 videos
• Statistical Learning Theory - cs229T/stats231 Stanford Autumn'18-19
• Statistical Learning Theory - cs281b /stat241b UC Berkeley, Spring'14
• Statistical Learning Theory - csc2532 Uni of Toronto, Spring'20

DL >>

• Deep Learning and Bayesian Methods - summer school, videos available for 2019 version
• Deep Unsupervised Learning - cs294
• Deep Multi-task and Meta learning - cs330
• Topics in Deep Learning - stat991 UPenn/Wharton *most chapters start with introductory topics and dig into advanced ones towards the end.
• Deep Generative Models - cs236
• Deep Geometric Learning of Big Data and Applications

DL >> Theory

• Topics course on Mathematics of Deep Learning - CSCI-GA 3033
• Topics Course on Deep Learning - stat212b
• Analyses of Deep Learning - stats385, videos from 2017 version
• Mathematics of Deep Learning
• Geometry of Deep Learning

RL >>

• Deep Reinforcement Learning - cs285
• Advanced robotics - cs287
• Reinforcement Learning - cs234, videos for 2019 run
• Reinforcement Learning Summer School 2019: Bandits, RL & Deep RL

Optimization >>

• Convex Optimization I - ee364a, has quite recent videos too. Convex Optimization II - ee364b, 2008 videos
• Convex Optimization and Approximation - ee227c
• Convex Optimization - ee227bt
• Variational Methods for Computer Vision, might not be super relevant to ML but functional optimization is a thing too ;)
• Advanced Optimization and Randomized Algorithms - 10-801, videos

Applications >> Computer Vision

• Computational Video Manipulation - cs448v
• Advanced Topics in ML: Modeling and Segmentation of Multivariate Mixed Data

Applications >> Natural Language Processing

• Natural Language Processing with Deep Learning - cs224n (* not sure if it belongs here, people working in NLP can help me out)
• Neural networks for NLP - cs11-747
• Natural Language Understanding - cs224u, video

Applications >> 3D Graphics

• Machine Learning for 3D Data - cs468
• Geometric Deep Learning - Not a course but the website links a few tutorials on Geometric DL
• Deep Learning for Computer Graphics - SIGGRAPH 2019
• Machine Learning for Machine Vision as Inverse Graphics - csc2547 Winter'20

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Edit: Upon suggestion, categorized the courses. There might be some misclassifications as I'm not trained on this task ;). Added some good ones from older (linked above) discussions.