Courses that I took from September 2020 to June 2024 during my time at U of T. I took notes for time series and regression which probably have mistakes.
Mathematics
- APM462: Nonlinear Optimization
- APM421: Mathematical Foundations of Quantum Mechanics and Quantum Information Theory
- MAT1510: Deep Learning: Theory & Data Science
- MAT477: Seminar in Mathematics
- MAT461: Hamiltonian Mechanics
- MAT457: Advanced Real Analysis I
- MAT377: Mathematical Probability. D. Panchenko: Introduction to Probability Theory
- MAT367: Differential Geometry. Spivak: A Comprehensive Introduction to Differential Geometry, Vol. 1
- MAT357: Foundations of Real Analysis. C. Pugh: Real Mathematical Analysis
- MAT354: Complex Analysis I. L. Ahlfors: Complex Analysis, H. Cartan: Elementary Theory of Analytic Functions of One Or Several Complex Variables.
- MAT351: Partial Differential Equations. W. Strauss: Partial Differential Equations: An Introduction
- MAT344: Introduction to Combinatorics. M. Kelle and T. Trotter: Applied Combinatorics
- MAT327: Introduction to Topology. J. Munkres: Topology
- MAT315: Introduction to Number Theory. G. Jones and M. Jones: Elementary Number Theory
- MAT267: Advanced Ordinary Differential Equations. M. Hirsch, S. Smale, and R. Devaney: Differential Equations, Dynamical Systems, and an Introduction to Chaos
- MAT257: Analysis II. M. Spivak: Calculus on Manifolds
- MAT247: Algebra II.S. Axler: Linear Algebra Done Right
- MAT240: Algebra I. S. Axler: Linear Algebra Done Right
- MAT157: Analysis I. M. Spivak: Calculus
Statistics
- STA457: Time Series Analysis
- STA414: Statistical Methods for Machine Learning II
- STA314: Statistical Methods for Machine Learning. G. James, D. Witten, T. Hastie, and R. Tibshirani: Introduction to Statistical Learning
- STA304: Surveys, Sampling, and Observational Data
- STA303: Methods of Data Analysis II
- STA302: Methods of Data Analysis I
- STA261: Probability and Statistics II
- STA257: Probability and Statistics I