Courses and schedules - Fall 2023
|Professor||Language of the course||Course abbreviation and name||Description||Crédits||Schedule||Start date||End date||Location|
|Simon Lacoste-Julien||English||IFT 6269 : Probabilistic Graphical Models||System Representation as probabilistic graphical models, inference in graphical models, learning parameters from data.||4||Tuesday and Thursday from 2:30 to 4:30 pm||September 5th||December 8th||Mila – Auditorium 1 & 2|
|Ioannis Mitliagkas||English||IFT 6390: Fondements de l’apprentissage machine||Basic elements of statistical and symbolic learning algorithms. Examples of applications in data mining, pattern recognition, nonlinear regression, and time data.||4|
|Sarath Chandar||English||INF8250AE – Reinforcement Learning||Designing autonomous decision making systems is one of the longstanding goals of Artificial Intelligence. Such decision making systems, if realized, can have a big impact in machine learning for robotics, game playing, control, health care to name a few. This course introduces Reinforcement Learning as a general framework to design such autonomous decision making systems. By the end of this course, you will have a solid knowledge of the core challenges in designing RL systems and how to approach them.||4||Monday from 12:45 to 3:45 pm||August 28, 2023||Poly L-1710|
|Jackie C. K. Cheung||English||COMP 550 – Natural Language Processing||Computer Science (Sci) : An introduction to the computational modelling of natural language, including algorithms, formalisms, and applications. Computational morphology, language modelling, syntactic parsing, lexical and compositional semantics, and discourse analysis. Selected applications such as automatic summarization, machine translation, and speech processing. Machine learning techniques for natural language processing.||3|
|Reihaneh Rabbany||COMP 599 – Network Science||An introduction to Network Science, this is a half lecture half seminar course. Networks model the relationships in complex systems, from hyperlinks between web pages, and co-authorships between research scholars to biological interactions between proteins and genes, and synaptic links between neurons. Network Science is an interdisciplinary research area involving researchers from Physics, Computer Science, Sociology, Math and Statistics, with applications in a wide range of domains including Biology, Medicine, Political Science, Marketing, Ecology, Criminology, etc. In this course, we will cover the basic concepts and techniques used in Network Science, review the state of the art techniques, and discuss the most recent developments.||3||Tue-Thu from 10 to 11:30 am||In person at McGill with accomodations for online participation|
|Guy Wolf||English / bilingual||MAT 6493 – Geometric data analysis||Formal and analytic approaches for modeling intrinsic geometries in data. Algorithms for constructing and utilizing such geometries in machine learning. Applications in classification, clustering, and dimensionality reduction.
The course will accommodate anglophone students who do not speak French, as well as francophone students.
|4||Tuesday & Thursday from 3:30 to 5:20||UdeM : 5183 Pav. Andre-Aisenstadt|
|Sarath Chandar||English||INF8245E – Machine Learning||This course provides a rigorous introduction to the field of machine learning (ML). The aim of the course is not just to teach how to use ML algorithms but also to explain why, how, and when these algorithms work. The course introduces fundamental algorithms in supervised learning and unsupervised learning from the first principles. The course, while covering several problems in machine learning like regression, classification, representation learning, dimensionality reduction, will introduce the core theory, which unifies all the algorithms.||3||Wednesday from 9:30 am to 12:30 pm||August 30, 2023||Poly M-1510|
|Gauthier Gidel + Glen Berseth||English||IFT 6758 – Data Science||The goal of this course is to introduce the concepts (theory and practice) needed to approach and solve data science problems. The first part of the course will cover the principles of analyzing data, the basics about different kinds of models and statistical inference. The second part expands into the statistical methods and practical techniques to deal with common modalities of data – image, text and graphs. Specific programming frameworks required for data science will be covered in the lab sessions.||4||Ma 11:30 – 12:30 pm
Je 4:30- 6:30 pm
Labo – Ma : 12:30 – 2:30 pm
|Pierre-Luc Bacon||EN||IFT 6162 Reinforcement Learning and Optimal Control (Apprentissage par renforcement, commande optimale)||Advanced course in reinforcement learning. Topics: Policy gradient methods, gradient estimation, analysis of valued-based function approximation methods, optimal control and automatic differentiation, bilevel optimization in meta-learning and inverse reinforcement learning.||4||Tuesday & Thursday
from 12:30 to 2:30 pm
|Mila – Auditorium 1|
|Aishwarya Agrawal||EN||IFT 6135 — Representation Learning||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, natural language processing 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 LSTMs. We will also review recent work on attention mechanism and efforts to incorporate memory structures into neural network models. We will also consider some of the modern neural network-base generative models such as Generative Adversarial Networks and Variational Autoencoders.
|4||Wednesday and Friday from 1:30 to 3:30 pm||September 6th, 2023||End of November / beginning of December||Mila – Auditorium 1 & 2|