Resources

Some of my notes

MATH240 Discrete Structure A complete note of material covered in the first course in discrete mathematics.
MATH323 Probablity (Extended) Probability notes, extended from material that usually covered in MATH323 to bridge the gap between between MATH323 and some advanced courses. (Not fully finished yet…)

Random stuffs

Customized Tufte Handout Latex The Latex template I used.

Resources I found useful

Mathematics

General purpose for McGill students:
SUMS Notesbank A public repository of McGill Math lecture notes made by students.

Linear Algebra:
Linear Algebra Done Right by Sheldon Axler.
Applied Linear Algebra for Theoretical Neuroscience by Ken Miller and Philip Sabes.

Multivariable/Vector Calculus:
Calculus Blue by Robert Ghrist.
MATH248 Honours VectorCalculus by Dr. Gantumur Tsogtgerel.

Probability Theory:
Introduction to Probability by David F. Anderson, Timo Seppäläinen, Benedek Valkó.
Probability Theory and Examples by Rick Durrett.
Stochastic Processes (MATH547) Lecture Notes by Elliot Paquette.

Optimization:
Optimization for Machine Learning (EPFL-CS-439).
Stochastic optimization: Algorithms, convergence, and techniques by Andre Milzarek.

Computer Science

Algorithms and Data Structures:
Algorithms and Data Structures (COMP252) by Luc Devroye. This proof-based CS course teaches both CS and applied math.

Machine Learning:
A statistical tour of physics-informed machine learning by Claire Boyer and Nathan Doumèche. Includes general introductive lectures on physics-informed neural networks (PINNs), kernel methods in machine learning and RKHS.

Miscellaneous/Engineering

Programming/High Performance Computing/Computer Engineering:
Guide for Using the Compute Canada clusters by Prashant Pandey.