Machine Learning & AI
Lectures
- Artificial Intelligence (MIT 6.034)
- Practical Data Science (CMU) Vids
- Machine Learning for Decision Making (Cornell CS4780)
- Machine Learning (Stanford CS229)
- Statistical Methods for Machine Learning (CMU)
- Reinforcement Learning (Deep Mind)
- Reinforcement Learning (Stanford CS234)
- Practical Machine Learning (fast.ai)
- Practical Deep Learning (fast.ai)
- Neural Networks for Machine Learning (Hinton)
- Neural Networks (Larochelle)
- Deep Learning (Stanford CS230)
- Deep Learning (Oxford)
- CNNs for Visual Recognition (Stanford CS231n) 2016
- Deep Reinforcement Learning
- Advanced Deep Learning & Reinforcement Learning (Deep Mind)
- Deep Reinforcement Learning
- Deep Unsupervised Learning
- Deep Multi-Task and Meta Learning (Stanford CS330)
- NLP with Deep Learning (Stanford CS224N)
- Probablistic Graphical Models
- Fundamentals of Machine Learning over Networks
- Frontiers in Deep Learning (Simons Institute)
- Emerging Challenges in Deep Learning (Simons Institute)
- Bayesian Methods in Machine Learning (Bayes Group)
Notes
- Machine Learning (Stanford CS229)
- Theoretical Foundations of Deep Learning (Arora)
- New Directions in Theoretical Machine Learning (Arora)
- Probablistic Graphical Models (Stanford CS228)
- Causal inference in statistics (Pearl)
- Optimization for Machine Learning (Hazan)
- Machine Super Intelligence (Legg)
Books
- Artificial Intelligence: A Modern Approach (Russell & Norvig)
- Pattern Recognition and Machine Learning (Bishop)
- The Elements of Statistical Learning (HTF)
- Reinforcement Learning (Sutton & Barto)
- Deep Learning (Goodfellow et al.)
- Computer Vision: Algorithms and Applications (Szeliski)
- Causality: Models, Reasoning and Inference (Pearl)
- Universal Artificial Intelligence (Hutter)
- Neuro-Dynamic Programming
- Multiagent Systems
Math & CS
Lectures
- Linear Algebra (Strang)
- Computational Linear Algebra
- Introduction to Algorithms (MIT)
- Advanced Algorithms (Harvard)
- Bayesian Data Analysis
- Convex Optimization (Stanford EE364A)
- Convex Optimization II (Stanford EE364B)
- Advanced Optimization and Randomized Algorithms
Books
- Introduction to Linear Algebra (Strang)
- Introduction to Applied Math (Strang)
- Linear Algebra Done Right
- Introductory Real Analysis
- Numerical Linear Algebra
- Matrix Computations
- Bayesian Data Analysis
- Advanced Data Analysis
- Real Analysis and Probability
- Mathematical Foundations of Infinite-Dimensional Statistical Models
- Playing for Real: A Text on Game Theory
- Algorithmic Game Theory
- Mathematics of Machine Learning
- A Probablistic Theory of Pattern Recognition
- Information Theory
- Convex Optimization
- Combinatorial Optimization
- Introduction to Algorithms (CLRS)
- An Introduction to Kolmogorov Complexity and Its Applications
Other Books & Notes
PhD
- How to Have a Bad Career in Research/Academia (David Patterson)
- A Survival Guide to a PhD (Andrej Karpathy)
- Doing well in your courses (Andrej Karpathy)
- How to Be a Leader in Your Field (Philip Agre)
- Productivity for Academics (Matt Might)
- Readings for Graduate Students (Matt Might)
- PhD Advice (Philip Guo)
- CS Grad School Survival Guide (Ron Azuma)
- Writing a scientific paper (John Martinis)
- How to Lecture (Patrick Winston)
- How to Give an Academic Talk (Paul Edwards)
- Classic AI Research Guide (CSAIL Alumni)