This is a course on representation learning in general and deep learning in particular. Deep learning has recently been responsible for a large number of impressive empirical gains across a wide array of applications including most dramatically in object recognition and detection in images and speech recognition.

In this course we will explore both the fundamentals and recent advances in the area of deep learning. Our focus will be on neural network-type models including convolutional neural networks and recurrent neural networks such as the LSTM. We will also consider some probabilistic graphical models, including undirected models such as the Boltzmann machines and directed models that have recently shown promise.

Instruction style: For the most part, this will be a “flipped class”. We will spend approx. 50% of the class time working through questions. Students are responsible for keeping up-to-date with the course material outside of class time. The material to be reviewed for each class will be made available on the course website.

Département d’informatique et recherche opérationnelle (DIRO)
Université de Montréal

Instructor:  Prof. Aaron Courville
Teaching assistant: Ph.D. student Vincent Dumoulin

Course plan

Disponible en français ici.
Available in english here.

Class schedule (locations)

Mondays: 2:30 – 4:30 PM (Z-210 Pav. Claire-McNicoll)
Thursdays: 9:30 – 11:30 AM (1177 Pav. André-Aisenstadt)
Exceptionally: Thursday, Feb. 19th (Z-300 Pav. Claire-McNicoll)