CSC311 Introduction to Machine Learning
This is a collection of notes, interactive visualizations, and resources for CSC311 Introduction to Machine Learning at the University of Toronto.
Created by: Prof. Alice Gao†, Prof. Lisa Zhang†, Prof. Joshua Jung, Prof. Marina Tawfik, and Prof. Rahul G. Krishnan. (†Co-leads)
Chapters
| Chapter | Section | Title |
|---|---|---|
| Introduction | ||
| About this resource | ||
| 1 | 1.1 | Supervised Learning |
| 1.2 | k-Nearest Neighbours | |
| 2 | 2.1 | Decision Trees |
| 2.2 | Information Theory for Decision Tree | |
| 2.3 | Decision Tree Learning | |
| 3 | 3.1 | Linear Regression |
| 3.2 | Gradient Descent | |
| 3.3 | Feature Mapping | |
| 3.4 | Regularization | |
| 4 | 4.1 | Logistic Regression |
| 4.2 | Stochastic Gradient Descent | |
| 4.3 | Multi-Class Classification | |
| 4.4 | Limitations of Linear Models | |
| 5 | 5.1 | Introducing the Neuron |
| 5.2 | Multi-Layer Perceptron | |
| 5.3 | Expressiveness of Neural Networks | |
| 5.4 | Backpropagation | |
| 6 | 6.1 | Bias-Variance Decomposition |
| 6.2 | Ensemble Methods | |
| 7 | 7.1 | Probabilistic Modeling |
| 7.2 | Probabilistic Conditional Models | |
| 7.3 | Naïve Bayes | |
| 8 | 8.1 | Univariate Gaussian Discriminant Analysis |
| 8.2 | Multivariate Gaussian Distribution | |
| 8.3 | Multivariate Gaussian Discriminant Analysis | |
| 9 | 9.1 | Unsupervised Learning |
| 9.2 | k-Means Clustering | |
| 9.3 | Gaussian Mixture Models | |
| 10 | Principal Component Analysis | |
| 11 | 11.1 | AI Ethics |
| 11.2 | Algorithmic Fairness | |
| 12 | Machine Learning Operations | |
| 13 | Learning Theory |