This collection of notes aims to help myself learn Math & Stats efficiently. Since one course gives dozens of theorems and corollaries, sorting them into clean notes is usually a good way to include them in the knowledge network in my mind.
🖋 Complete Notes
🗝 STA447 Stochastic Processes (Winter 2020)
🗝 STA414 Statistical Methods for Machine Learning II (Winter 2020)
🗝 MAT337 Real Analysis (Winter 2020)
🗝 APM462 Nonlinear Optimizations (Fall 2019)
🗝 STA347 Probability I (Fall 2019)
🗝 STA302 Methods of Data Analysis (Summer 2019)
🗝 MAT224 Linear Algebra II (Winter 2019)
- With special comments about k-dimensional manifolds, which is more abstract than materials covered in lecture.
🗃 Archived Notes
- 🎓 CSC418 Computer Graphics (Summer 2020)
- Raster Images
- Ray Casting
- Ray Tracing
- Bounding Volume Hierachy
- Triangle Meshes
- Shader Pipeline
- Mass-Spring Systems
- 🎓 CSC413 Neural Networks and Deep Learning (Winter 2020)
- Introduction & Linear Models
- Multilayer Perceptrons & Backpropagation
- Automatic Differentiation & Distributed Representations
- Convolutional Neural Networks & Image Classification
- 🎓 CSC311 Introduction to Machine Learning (Fall 2019)
- k-Means and EM Algorithm
- Reinforcement Learning
- 🎓 STA261 (Winter 2019)
- Converge in distribution
- Normal Distribution Theory
- Expectation and Covariance
- Different Types of Estimation
- Sampling Distributions
- Consistent & Efficient Estimators
- 🎓 CSC263 Data Structures and Analysis (Winter 2019)
- Some notes on AVL trees
Unless otherwise stated, all files in this repo are licensed under Attribution-NonCommercial-ShareAlike 4.0 International. You can find the full legal code as well as its translations here. By the license, in human readable words, you are free to
- Share — copy and redistribute the material in any medium or format
- Adapt — remix, transform, and build upon the material
under the following terms:
- Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- NonCommercial — You may not use the material for commercial purposes.
- ShareAlike — If you remix, transform, or build upon the material, you must distribute your contributions under the same license as the original.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
- You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation. I.e., The rights of users under exceptions and limitations, such as fair use and fair dealing, are not affected by the CC Licenses.
- No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material.