## About CSC411/2515

This course serves as a broad introduction to machine learning and data mining. We will cover the fundamentals of supervised and unsupervised learning. We will focus on neural networks, policy gradient methods in reinforcement learning. We will use the Python NumPy/SciPy stack. Students should be comfortable with calculus, probability, and linear algebra.

## Teaching Team

Instructor | Section | Office Hour | ||
---|---|---|---|---|

Michael Guerzhoy | Th6-9 (SF1101) | M6-7, W6-7 (BA3219) | guerzhoy [at] cs.toronto.edu | |

Lisa Zhang | T1-3, Th2-3 (RW117) | Th10-12 (BA3219) | lczhang [at] cs.toronto.edu |

When emailing instructors, please include "CSC411" in the subject.

Please ask questions on Piazza if they are relevant to everyone.

There will also be TA office hourse (TBA)

## Study Guide

The CSC411 study guide.

## Tentative Schedule

Day | Night | Content | Notes/Reading | Deadlines | |
---|---|---|---|---|---|

Review of Probability | Deep Learning 3.1-3.9.3 | Math Background Problem Set
Complete ASAP | |||

Review of Linear Algebra | Deep Learning 2.1-2.6 | ||||

Week 1 | Jan 4 | Jan 4 | Welcome; K-Nearest Neighbours | Video: A No Free Lunch theorem | |

Jan 9 | Jan 4 | Linear Regression | Roger Grosse's Notes on Linear Regression
| ||

Jan 9 | Jan 4 | ||||

Week 2 | Jan 11 | Jan 11 | Numpy Demo:
html
ipynb
Numpy Images: html ipynb 3D Plots (contour plots, etc): html ipynb Gradient Descent (1D): html ipynb Gradient Descent (2D): html ipynb Linear Regression: html ipynb | What is the direction of steepest ascent on the point (x, y, z) on a surface plot? Solution (Video: Part 1, Part 2.) | |

Jan 16 | Jan 11 | Multiple Linear Regression Linear Classification (Logistic Regression) Maximum Likelihood Bayesian Inference | Andrew Ng's Notes on Logistic Regression Maximum Likelihood | ||

Jan 16 | Jan 11 | ||||

Week 3 | Jan 18 | Jan 18 | Bayesian inference and regularization (updated) Unicorns Bayesian inference: html ipynb Overfit in linear regression: html ipynb | Video: Unicorns and Bayesian Inference | |

Jan 22 | Jan 18 | Neural Networks |
| ||

Jan 22 | Jan 18 | ||||

Week 4 | Jan 25 | Jan 25 | Pytorch | Project #1 due Jan 29th | |

Jan 30 | Jan 25 | Convolutional Neural Networks | |||

Jan 30 | Jan 25 | ||||

Week 5 | Feb 1 | Feb 1 | Decision Trees & Probabilistic Classifiers | Project #1 bonus due Feb 5th | |

Feb 6 | Feb 1 | ||||

Feb 6 | Feb 1 | ||||

Feb 8 | Feb 8 | k-means; naive bayes | |||

Feb 13 | Feb 8 | ||||

Feb 13 | Feb 8 | ||||

Week 7 | Feb 15 | Feb 15 | Mixture of Gaussians | Project #2 due Feb 18th Grad Project Proposal due Feb 28th | |

Reading Week | Feb 27 | Feb 15 | |||

Feb 27 | Feb 15 | ||||

Week 8 | Mar 1 | Mar 1 | PCA | Midterm Mar 2nd 6pm-8pm | |

Mar 6 | Mar 1 | ||||

Mar 6 | Mar 1 | ||||

Week 9 | Mar 8 | Mar 8 | Reinforcement Learning | Project #3 due Mar 12 | |

Mar 13 | Mar 8 | ||||

Mar 13 | Mar 8 | ||||

Week 10 | Mar 15 | Mar 15 | SVMs and Kernels | ||

Mar 20 | Mar 15 | ||||

Mar 20 | Mar 15 | ||||

Week 11 | Mar 22 | Mar 22 | Ensembles | ||

Mar 27 | Mar 22 | ||||

Mar 27 | Mar 22 | ||||

Week 12 | Mar 29 | Mar 29 | TBD | Project #4 due Apr 2 | |

Apr 3 | Mar 29 | ||||

Apr 3 | Mar 29 | ||||

Final Examinations (undergraduate) | TBA | ||||

Graduate Project deadline (graduate) | TBA |