- Overview
- Instructors
- Lecture and Tutorial Times
- Prerequisites
- Communication
- COVID-19
- Marking Scheme
- Course Schedule
- Assignments
- Project
- Midterm
- Final Exam
- Academic Integrity
- Remark Request Policy
- Special Considerations Policy
- Student Support Resources
- Recommended Textbooks
- Lecture and Tutorial Materials
- Computing Resources

Machine learning (ML) is a set of techniques that allow computers to learn from data and experience rather than requiring humans to specify the desired behaviour by hand. ML has become increasingly central both in AI as an academic field and industry. This course provides a broad introduction to some of the most commonly used ML algorithms. It also introduces vital algorithmic principles that will serve as a foundation for more advanced courses, such as CSC412/2506 (Probabilistic Learning and Reasoning) and CSC413/2516 (Neural Networks and Deep Learning).

We start with nearest neighbors, the canonical nonparametric model. We then turn to parametric models: linear regression, logistic regression, softmax regression, and neural networks. We then move on to unsupervised learning, focusing in particular on probabilistic models, but also principal components analysis and K-means. Finally, we cover the basics of reinforcement learning.

As of Sep 2022, we plan to have in-person lectures, tutorials, office hours, midterm and final exam in the fall 2022 term. This may, given the COVID-19 situation, be subject to change by the university.

Please attend your assigned lecture section. We strongly encourage students to attend the tutorials although they are optional. Auditing is not allowed this term without express written permission by the instructor.

Section |
Lecture |
Tutorial |

LEC0101, LEC2001 | Wednesday 12 - 2pm at MC 254 by Rahul G. Krishnan | Friday 12 - 1pm at MC 254. Sept. 23: 12-1pm at Bahen 1170 |

LEC0201 | Tuesday 3 - 5pm at NL 6 by Alice Gao | Thursday 3 - 4pm at AH 400 |

- Programming Basics: CSC207/ APS105/ APS106/ ESC180/ CSC180
- Multivariate Calculus: MAT235/ MAT237/ MAT257/ (minimum of 77% in MAT135 and MAT136)/ (minimum of 73% in MAT137)/ (minimum of 67% in MAT157)/ MAT291/ MAT294/ (minimum of 77% in MAT186, MAT187)/ (minimum of 73% in MAT194, MAT195)/ (minimum of 73% in ESC194, ESC195)
- Linear Algebra: MAT221/ MAT223/ MAT240/ MAT185/ MAT188
- Probability: STA237/ STA247/ STA255/ STA257/ STA286/ CHE223/ CME263/ MIE231/ MIE236/ MSE238/ ECE286

- Piazza: piazza.com/utoronto.ca/fall2022/csc311h1f20229
- Course email address: csc311-2022-09@cs.toronto.edu
- Instructor and TA office hours

- If your question is
**course related and doesn't give away answers**, please post on Piazza publicly so the entire class can benefit from the answer. - If your question is
**course related and may give away answers**, please post on Piazza privately. - For
**remark requests**, please submit on MarkUs (for assignments) or contact us via the course email: csc311-2022-09@cs.toronto.edu. - For
**special considerations requests**, please contact us via the course email: csc311-2022-09@cs.toronto.edu.

- Rahul Krishnan: Tuesdays 9-10:30am at PT286
- Alice Gao: Wednesdays 4 - 5pm, Fridays 3 - 4pm at BA4240

Although the pandemic has diminished somewhat, all indications are that we are not yet out of the woods. The university no longer requires the use of masks on its premises but encourages it where it is impossible to maintain physical distancing, such as in classrooms and offices.

We strongly recommend that you continue to **wear masks** during lectures, tutorials, and office hours out of consideration for the health of others. We also strongly encourage you to **get vaccine booster shots** whenever possible. The instructors plan to wear masks when in close proximity with students, such as when answering questions after lectures or during office hours. However, we may take off our masks when lecturing if we are at a safe distance from students.

Component |
% Final Grade |

3 Assignments | 35% (~11.67% each) |

Ethics | 5% |

Project | 10% |

Midterm | 20% |

Final | 30% |

Assignment |
% Final Grade |
Marking |

Pre-module survey | 1% | Full credit for submitting. |

Class participation | 0.5% | You get this 0.5% automatically. |

Reflections on In-Class Activity | 2% | Full credit for a good-faith effort. |

Post-module survey | 1.5% | Full credit for submitting. |