Valuable Resources for machine learning, deep learning, reinforcement learning, etc.

Machine Learning

Reinforcement Learning

• KAIST 문일철 교수 강의

  • 머신러닝 기초
  • 머신러닝 심화

• David MacKay video lecture

  • Course on Information Theory, Pattern Recognition, and Neural Networks
  • Textbook: Information Theory, Inference, and Learning Algorithms

• Carl Edward Rasmussen video lecture

  • Bayesian inference and Gaussian processes

• Michael Zibulevsky video lecture

  • Introduction to Optimizationand Deep Learning

• Autoencoder

  • 오토인코더의 모든 것
  • Ali Ghodsi, Lec : Deep Learning, Variational Autoencoder

• David MacKay, Information Theory, Inference, and Learning Algorithms: [link]
• David Barber, Bayesian Reasoning and Machine Learning: [link]
• Shai Shalev-Shwartz and Shai Ben-David, Understanding Machine Learning: From Theory to Algorithms: [link]
• Carl Edward Rasmussen and Christopher K. I. Williams, Gaussian Processes for Machine Learning: [link]
• Ian Goodfellow, Yoshua Bengio and Aaron Courville, Deep Learning: [link]

• Gaussian Processes
  • http://efavdb.com/gaussian-processes/
  • http://bridg.land/posts/gaussian-processes-1
• Recurrent Neural Network (RNN)
  • http://karpathy.github.io/2015/05/21/rnn-effectiveness/

• Reinforcement Learning: Basic concepts - Joelle Pineau
  • Deep Learning Summer School, Montreal 2016
  • CIFAR Reinforcement Learning Summer School 2017
• Policy Search for RL - Pieter Abbeel
  • CIFAR Reinforcement Learning Summer School 2017
• Excellent short video clip about TRPO by Crazymuse AI
  • TRPO (Trust Region Policy Optimization) : In depth Research Paper Review
• David Silver's lecture
  • Tutorial: Deep Reinforcement Learning
  • DeepMind RL Lecture
• UC Berkeley CS 294: Deep Reinforcement Learning
  • http://rll.berkeley.edu/deeprlcourse/
• Deep RL Bootcamp, Berkeley CA, 26-27 August 2017
  • https://sites.google.com/view/deep-rl-bootcamp/lectures
• Pang-Yo Lab
  • RL Lecture in Korean based on David Silver's DeepMind Lecture
• Columbia University IEOR 8100: Reinforcement Learning by Shipra Agrawal (not video, but useful)
  • https://ieor8100.github.io/rl/

• Richard S. Sutton and Andrew G. Barto, Reinforcement Learning: An Introduction: [link]
• Dimitri P. Bertsekas, REINFORCEMENT LEARNING AND OPTIMAL CONTROL: [link]
  • Also many lecture videos and slides are available.
• Miguel Morales, Grokking Deep Reinforcement Learning: [link]
  • Can see the entire book for free by clicking the table of contents

• OpenAI Spinning Up
  • Spinning Up in Deep RL
• Policy Gradient
  • Deep Reinforcement Learning: Pong from Pixels by Andrej Karpathy
• TRPO
  • https://www.groundai.com/project/trust-region-policy-optimization/
• Proximal Policy Optimization (PPO)
  • http://blog.varunajayasiri.com/ml/ppo.html
• Model-based Reinforcement Learning
  • https://deepmind.com/blog/agents-imagine-and-plan/
  • http://bair.berkeley.edu/blog/2018/04/26/tdm/
• Multi-agent Reinforcement Learning
  • https://blog.openai.com/learning-to-communicate/
  • https://deepmind.com/blog/understanding-agent-cooperation/
• How to build your own AlphaZero AI using Python and Keras
• Teaching a machine to master car racing and fireball avoidance through “World Models”

• Convex Optimization
  • CMU 2018 lecture by Ryan Tibshirani