Machine Learning
Lawrence Saul: Principles of AI: Probabilistic Reasoning and Decision-Making
Kevin Murphy: Machine Learning: A Probabilistic Perspective
Nicholas Zabaras: Bayesian Scientific Computing
Schulman & Abbeel: Deep Reinforcement Learning
Gelman et al: Bayesian Data Analysis
Machine Learning and Computational Statistics (NYU)
Machine Learning (UW)
Free Ebooks:
Sutton & Barto: Reinforcement Learning: An Introduction (Matlab Code)
James et al.: Introduction to Statistical Learning: with application in R
David Barber: Bayesian Reasoning and Machine Learning
Rasmussen & Williams: Gaussian Processes for Machine Learning
Hastie, Tibshirani & Friedman: The Elements of Statistical Learning
Howard J. Seltman: Experimental Design and Analysis
Optimal Control Courses:
Emo Todorov: Neural Control of Movement: A Computational Perspective
Reza Shadmehr: Computational Motor Control Learning Theory
Pieter Abbeel: Advanced Robotics; Advanced Robotics 2012
Stephen Boyd: Optimal Control and Dynamic Programming
Planning Algorithms
Human Motion Analysis
Data Science
Computational Statistics in Python
Data science knowledge Repo
Kaggle
Statistics
Common Mistakes in Statistics
Video Lectures:
Kalman Filter
Neuroscience of Reinforcement Learning
Continuous Inverse Optimal Control with Locally Optimal Examples
Active Learning for Reward Estimation in Inverse Reinforcement Learning
Computational Rationalization: The Inverse Equilibrium Problem
Motivation & Emotion:
Affective Neuroscience
Motivation and Emotion
Causality
Causality by Judea Pearl
Psychiatry-Depression
Depression changes the brain
Psychiatry and Neurobiology Slide Library
McMan's Depression and Bipolar Web
Apathy and Anhedonia in Depression- Pizzagalli
Coding
Learn Python The Hard Way
Others
(When does Bayesian model perform pooly) StackExchange_CognitiveScience
Minitab
Cafe Scientifique
Traffic Safety
Interesting blogs
Pythonic Perambulations