Search
K.B.
Machine Learning courses
My course recommendations for machine learning.
Jul 5, 2020
Last updated: Apr 15, 2022
🧮 math
Table of Contents
Courses
Linear Algebra
Reinforcement Learning
Control
Learning Theory
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
© 2023 Sanyam Kapoor