This folder includes miscellaneous notes I prepared, compiled, or took when I hosted/attended seminars, took classes, or read books. Some only include coursework/homework of the course without notes.
Topics included are quite broad: Machine Learning, Theoretical Machine Learning, Theoretical Statistics,
Nonlinear Dimension Reduction (Manifold Learning), TDA (Topological Data Analysis), just to name a few.
For easy reference, I list the topics of my notes below according to their relevance to each other and include where to find the notes:
- Machine Learning: Fall 2016 (Edx Course)
- Theoretical Machine/Statistical Learning: Fall 2017
- Statistics: Winter 2017, Spring 2018
- Deep Learning with Keras: Spring 2019 (Edx Course)
- Deep Learning: Fall 2017 (IST Course)
- Deep Learning Specialization: Summer 2020 (Coursera 5-Course Specialization)
- Stochastic Processes: Spring 2017
- Stochastic Calculus: Spring 2018
- Stochastic Differential Equations: Spring 2019
- Applied Time Series Analysis: Spring 2019 (Coursera Course)
- Theories behind Convex Optimization and Linear Programming: Spring 2019
- Probability Theory (measure theory based): Spring 2016, Fall 2016 (Course)
- Measure Theory (Real Analysis): Spring 2016
- Nonlinear Dimensionality Reduction (Manifold Learning): Spring 2017, Spring 2018, Fall 2018
- Topological Data Analysis: Spring 2017
- Applied Algebraic Topology: Fall 2015 (Course), Spring 2018 (Course)
- Algebraic Topology: Spring 2016
- Riemannian Manifolds: Spring 2018
- Differentiable Manifolds: Spring 2017 (Course)
- Numerical Analysis: Fall 2017 (Course)
- Abstract Algebra: Spring 2016 (Course)