Machine Learning courses

My course recommendations for machine learning.

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Courses

• Introduction to Computational Thinking by Alan Edelman, David P. Sanders & Charles E. Leiserson (originally also by Grant Senderson)
• 100 Lectures on Machine Learning by Mark Schmidt
• Optimization for Data Science by Yao-Liang Yu
• Information Theory, Pattern Recognition and Neural Networks by David MacKay
• Discrete Differential Geometry by Keenan Crane
• Non-linear Dynamics and Chaos by Shane Ross
• Deep Learning Systems by Tianqi Chen and Zico Colter

Linear Algebra

• Linear Algebra by Gilbert Strang
• Matrix Methods in Data Analysis, Signal Processing, and Machine Learning by Gilbert Strang
• Matrix Computations by Austin Benson
• Numerical Methods for Data Science by David Bindel

Reinforcement Learning

• Reinforcement Learning by David Silver
• Reinforcement Learning Course by Dimitri P. Bertsekas
• Statistical Reinforcement Learning by Nan Jiang

Control

• Control Bootcamp by Steve Brunton
• Slotine Lectures on Nonlinear Systems by Jean-Jacques Slotine

Learning Theory

• Learning Theory from First Principles by Francis Bach
• Deep Learning Theory Lecture Notes by Matus Telgarsky
• New Directions in Theoretical Machine Learning by Sanjeev Arora