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- 11: Neuroevolution - The Nature of Code
- coursera
- ML Crash course by Google
- ML Recipes course by Josh Gordon
- The Neural Aesthetic (Gene Kogan)
- 01 Introduction, the whole course "in 60 minutes"
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Statistics - Harvard University: Statistics 110 | more videos on their YouTube channel
- Stanford University
- Statistical Inference [course]
Deep Learning
http://web.stanford.edu/class/cs230/
[ Natural Language Processing ]
CS 124: From Languages to Information (LINGUIST 180, LINGUIST 280)
http://web.stanford.edu/class/cs124/
CS 224N: Natural Language Processing with Deep Learning (LINGUIST 284)
http://web.stanford.edu/class/cs224n/
CS 224U: Natural Language Understanding (LINGUIST 188, LINGUIST 288)
http://web.stanford.edu/class/cs224u/
CS 276: Information Retrieval and Web Search (LINGUIST 286)
http://web.stanford.edu/class/cs276
[ Computer Vision ] CS 131: Computer Vision: Foundations and Applications
http://cs131.stanford.edu
CS 205L: Continuous Mathematical Methods with an Emphasis on Machine Learning
http://web.stanford.edu/class/cs205l/
CS 231N: Convolutional Neural Networks for Visual Recognition
CS 348K: Visual Computing Systems
http://graphics.stanford.edu/courses/cs348v-18-winter/
[ Others ]
CS224W: Machine Learning with Graphs(Yong Dam Kim )
http://web.stanford.edu/class/cs224w/
CS 273B: Deep Learning in Genomics and Biomedicine (BIODS 237, BIOMEDIN 273B, GENE 236)
https://canvas.stanford.edu/courses/51037
CS 236: Deep Generative Models
https://deepgenerativemodels.github.io/
CS 228: Probabilistic Graphical Models: Principles and Techniques
CS 337: Al-Assisted Care (MED 277)
CS 229: Machine Learning (STATS 229)
CS 229A: Applied Machine Learning
CS 234: Reinforcement Learning
http://s234.stanford.edu
CS 221: Artificial Intelligence: Principles and Techniques
https://stanford-cs221.github.io/autumn2019/
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