Fall 2024 - UTM - CSC 413: Neural Networks and Deep Learning
Course Overview
It is very hard to hand-design programs to solve many real-world problems, e.g. distinguishing images of cats v.s. dogs. Machine learning algorithms allow computers to learn from example data, and produce a program that does the job. Neural networks are a class of machine learning algorithms originally inspired by the brain, but which have recently have seen a lot of success at practical applications. They’re at the heart of production systems at companies like Google and Facebook for image processing, speech-to-text, and language understanding.
This course gives an overview of both the foundational ideas and the recent advances in neural net algorithms. Roughly the first 2/3 of the course focuses on supervised learning - training the network to produce a specified behavior when one has lots of labeled examples of that behavior. The last 1/3 focuses on unsupervised learning.
Prerequisites
Prerequisite: CSC311H5 or CSC411H5
Exclusion: CSC321H5 or CSC321H1 or CSC413H1 or CSC421H1 (SCI)
Distribution Requirement: SCI
Students who lack a pre/co-requisite can be removed at any time unless they have received an explicit waiver from the department. The waiver form can be downloaded from here.
Course Delivery Details
| Lectures | Prof | Day | Time | Location |
|---|---|---|---|---|
| LEC0101 | Igor Gilitschenski | Tuesday | 5:00 pm - 7:00 pm | MN3190 |
| LEC0102 | Florian Shkurti | Wednesday | 11:00 am - 1:00 pm | MN3190 |
| Tutorials/Labs | Day | Time | Location |
|---|---|---|---|
| PRA0101 | Friday | 10:00 am - 11:00 am | DH2020 |
| PRA0102 | Friday | 11:00 am - 12:00 pm | DH2020 |
| PRA0103 | Friday | 12:00 pm - 1:00 pm | DH2020 |
- Announcements will be posted on Quercus
- Discussions will take place on Piazza
- Zoom recordings will be posted on Quercus after lectures