- 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

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

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

- 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