Machine Learning UW CSE 🤖
Human-AI Interaction and Community Awareness (NIPS 2019)
Hey, I took an intro CSE course at the University of Washington and realized that all of their course information is online. I had some interest in machine learning so I looked it up and parsed the courses + special topics, making a list of them in the past. I remembered it today and thought it might be useful to share.
Berkeley and Stanford also have good parsable class info that could be really helpful too.
- CSE 546 Machine Learning
- CSE 515 - Statistical Methods in Computer Science - Winter 2018
- CSE 573 - Introduction to Artificial Intelligence - Winter 2017
- CSE/EE 576: Computer Vision, Spring 2018
- CSE 517 - Natural Language Processing - Winter 2017
- CSE 599 - Advanced Natural Language Processing - Spring 2015
- CSE 571: Robotics
- CSE 527 Computational Biology
- CSE 599I: Accelerated Computing - Programming GPUs
- CSE 599W: Systems for ML
- CSE 599s — Hardware/Software Co-Optimization for Machine Learning
ResNet, we got to go deeper. Like WAY DEEPER.
Why UW? UW keeps it’s past year sites. UW has a lot of machine learning and AI research. UW is originally where I went and learned about CS.
Berkeley would be second best, their classes are good, they keep their material easily searchable, and are getting a CS 182 (CS 194-129) Deep Learning Class! Their CS 294-112 Deep Reinforcement Learning class looks awesome too! I do have the videos for the other machine learning classes but can’t share them. I also have watched Jonathan Shewchuk’s CS 189: Machine Learning and taken CS 188: AI and really enjoyed them. As a semester school, they go way more in depth, which I like as a student but when getting a sense of new stuff, more breadth can be useful.
Stanford has way more niche stuff. I really find that cool. But they don’t seem to host previous year material in easily searchable/parsable ways. That’s a shame. CS 231n is really good! CS 182 (CS 194-129) at Berkeley uses a lot of that.
Regardless, it’s a good tool for self-learning!