Practicable Course List (continually updated)

The main areas of concern are:

Natural Language Processing
Machine Learning and Deep Learning
Reinforcement Learning
Foundation of Mathematics

NLP

Stanford，CS224n
- http://web.stanford.edu/class/cs224n/index.html
- video：Winter 2019
CMU，CS 11-731
- Machine Translation and Seq-to-Seq Models，2018
- http://www.phontron.com/class/mtandseq2seq2018/
CMU，CS 11-747
- Neural Networks for NLP，Spring 2018
- link:
  - 2018
  - spring,2020
- video：2019
Oxford
- https://github.com/oxford-cs-deepnlp-2017/lectures
Berkeley
- Applied Natural Language Processing
- http://people.ischool.berkeley.edu/~dbamman/info256.html
Penn，CIS 700
- Advanced Machine Learning for Natural Language Processing，Dan Roth
- http://www.cis.upenn.edu/~danroth/Teaching/CIS-700-006/index.html
CS 4650 and 7650
- https://github.com/jacobeisenstein/gt-nlp-class
University of Washington
- CSE 447/547M: Natural Language Processing
- https://courses.cs.washington.edu/courses/cse447/19wi/
CS 521: Statistical Natural Language Processing
- by Natalie Parde at the University of Illinois;
- introduces advanced topics in statistical and neural NLP and provides an overview of active research in those topic areas
- link

Stanford，CS229
- http://cs229.stanford.edu/index.html#info
李宏毅
- http://speech.ee.ntu.edu.tw/~tlkagk/courses.html
Berkeley，STAT 157
- Introduction to Deep Learning
- http://courses.d2l.ai/berkeley-stat-157/index.html
Berkeley，2019，无监督学习
- CS294-158 Deep Unsupervised Learning Spring 2019
- https://sites.google.com/view/berkeley-cs294-158-sp19/home
Stanford，CS 236: Deep Generative Models
- https://deepgenerativemodels.github.io/
MIT，6.883
- Science of Deep Learning：Bridging Theory and Practice
- https://people.csail.mit.edu/madry/6.883/
Maching Learning Summer School
- http://mlss.cc/

UCL，David Sliver
- http://www0.cs.ucl.ac.uk/staff/d.silver/web/Teaching.html
Berkeley S294-112
- Deep Reinforcement Learning
- http://rail.eecs.berkeley.edu/deeprlcourse/
Stanford
- CS234: Reinforcement Learning Winter 2019
- http://web.stanford.edu/class/cs234/
Berkeley CS188
- Introduction to Artificial Intelligence
- https://inst.eecs.berkeley.edu/~cs188/sp19/

Stanford，CS229T/STATS231
- Statistical Learning Theory
- https://web.stanford.edu/class/cs229t/
CMU，10-708，Probabilistic Graphical Models
- Eric Xing， Spring 2014
- http://www.cs.cmu.edu/~epxing/Class/10708/lecture.html
NYU，MathsDL-spring18
- Topics course Mathematics of Deep Learning, NYU, Spring 18. CSCI-GA 3033.
- https://github.com/joanbruna/MathsDL-spring18