## 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.

TA office hours are listed here.

## 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 |
| |

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 |
| |

Jan 16 | Jan 11 | Multiple Linear Regression Linear Classification (Logistic Regression) Maximum Likelihood Bayesian Inference | Andrew Ng's Notes on Logistic Regression Andrew Ng, CS229 notes | ||

Jan 16 | Jan 11 | ||||

Week 3 | Jan 18 | Jan 18 | Bayesian inference and regularization (updated) Unicorns Bayesian inference: html ipynb Overfitting in linear regression: html ipynb | Videos: Unicorns and Bayesian Inference, Why I don't believe in large coefficients, Why L1 regularization drives some coefficients to 0 | |

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

Jan 22 | Jan 18 | ||||

Week 4 | Jan 25 | Jan 25 | PyTorch Basics ipynb, html; Maximum Likelihood with PyTorch (ipynb, html) ; Neural Networks in PyTorch, low-level programming (ipynb, html) and high-level programming (ipynb, html); If there is time, Justin Johnson's Dynamic Net (ipynb, html) |
| Project #1 due Jan 29th |

Jan 30 | Jan 25 | Neural Networks, continued. Neural Networks Optimization, Activation functions, multiclass classification with maximum likelihood |
| ||

Jan. 30 | Jan. 25 | ||||

Week 5 | Feb 1 | Feb 1 |
| Project #1 bonus due Feb 5th | |

Feb 6 | Feb 1 | ||||

Feb 6 | Feb 1 | Reading: CIML Ch. 8, Bishop 4.2.1-4.2.3, Andrew Ng, Generative Learning Algorithms | |||

Week 6 | Feb 8 | Feb 8 | Gaussian classifiers, Multivariate Gaussains (ipynb), Mixtures of Gaussians and k-Means |
| |

Feb 13 | Feb 8 | ||||

Feb 13 | Feb 8 | ||||

Week 7 | Feb 15 | Feb 15 | EM Tutorial: Gaussians, Binomial. | | Project #2 due Feb 23rd Grad Project Proposal due Feb 28th |

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

Feb 27 | Feb 15 | ||||

Week 8 | Mar 1 | Mar 1 | Midterm Review Tutorial (8pm-9pm for evening section) | Midterm Mar 2nd 6pm-8pm | |

Mar 6 | Mar 1 | Decision Trees | |||

Mar 6 | Mar 1 | ||||

Week 9 | Mar 8 | Mar 8 | Reinforcement Learning | ||

Mar 13 | Mar 8 | ||||

Mar 13 | Mar 8 | ||||

Week 10 | Mar 15 | Mar 15 | SVMs and Kernels | Project #3 due Mar 19 | |

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 Examination (CSC411) | TBA | ||||

CSC2515 Project | due Apr. 15 |