A collection of resources for self-studying machine learning, with a focus on mathematics. An attempt is made to select canonical math textbooks, but they are primarily selected on the basis of the availability of corresponding video lectures. This list is not meant to be comprehensive, but rather focused and tailored to my own goals.
| Topic | Lecture Videos | Textbook |
|---|---|---|
| Multivariable Calculus and Linear Algebra | Math 23a (no longer public) | Vector Calculus, Linear Algebra, and Differential Forms: A Unified Approach by Hubbard and Hubbard |
| Real Analysis | Real Analysis: Lectures by Professor Francis Su Principles of Mathematical Analysis: Winston Ou |
Principles of Mathematical Analysis by Walter Rudin |
| Algebra | Math 122 | Algebra by Michael Artin |
| Topology | Topology by Bruno Zimmerman | Topology by James Munkres |
| Algebraic Topology | Algebraic Topology - Pierre Albin Algebraic Topology - N J Wildberger |
Algebraic Topology by Allen Hatcher |
| Category Theory | Category Theory Foundations - Steve Awodey | Category Theory by Steve Awodey |
| Title | University | Programming Language |
|---|---|---|
| Deep Learning Specialization | Stanford | Python |
| Statistical Learning | Stanford | R |
| Learning from Data | Caltech | Any |
| Neural Networks for Machine Learning | University of Toronto | Any |
| Machine Learning | Columbia | Python, MATLAB |
| Probabilistic Graphical Models | Stanford | MATLAB/Octave |
| Machine Learning with Python | MIT | Python |
| Machine Learning for Healthcare | MIT | Python |
- Software Construction in Java, MIT
- Advanced Software Construction in Java, MIT
- Algorithms I and Algorithms II, Princeton University
- Algorithms Specialization, Stanford University
- Convex Optimization, Stanford University
- Automata Theory, Stanford University
- Introduction to Probability - The Science of Uncertainty, MIT
- Advanced Linear Models for Data Science 1: Least Squares and Advanced Linear Models for Data Science 2: Statistical Linear Models, Johns Hopkins University
- SciPy 2016: Scientific Computing with Python Conference
- JMLR
- Deep Learning papers
- Practical Deep Learning for Coders, Part 1
- 6.S094: Deep Learning for Self-Driving Cars
- CS224d: Deep Learning for Natural Language Processing
- CS231n: Convolutional Neural Networks for Visual Recognition
- CS 294: Deep Reinforcement Learning, Spring 2017
- Bayesian Data Analysis Course