Mila > Courses

Courses

Courses and schedules - Winter 2024

 

Professor Language of the course Course abbreviation and name Description Crédits Schedule Start date End date Location
Simon Lacoste-Julien IFT 6132 – Advanced Structured Prediction and Optimization This is an advanced machine learning course that will focus on the fundamental principles and related tools for structured prediction. 4 Tuesday and Thursday 9:30-11:30am Winter 2024 (to be determined) Mila – Auditorium 1
Aaron Courville EN/FR IFT6135 – Representation Learning 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. 4 ENG Tuesday and Thursday 3:30-5:30pm

FR Monday and Wednesday 2:30-4:30pm

January 8, 2024 April 15, 2024 Mila, if possible.
Guillaume Rabusseau EN IFT 6166 – Matrix and tensor factorization for ML The goal of this course is to present an overview of linear and multilinear algebra techniques for designing/analyzing ML algorithms and models, and to engage students with new research in the area. 4 Monday and Tuesday 9:30-11:30am January 8, 2024 Mila, Auditorium 2
Irina Rish EN IFT6760A: Towards AGI: Scaling, Alignment and Emergent Behaviors in Neural Nets This seminar-style course will focus on recent advances in the rapidly developing area of “foundation models”, i.e. large-scale neural network models (e.g., GPT-3, CLIP, DALL-e, etc) pretrained on very large, diverse datasets. Monday and Wednesday 4:30pm-6:30pm Winter 2024 (to be determined) Mila, Auditorium 2
Guillaume Lajoie EN/FR MAT 6215 – Dynamical Systems This graduate course is an introduction to the treatment of nonlinear differential equations, and more generally to the theory of dynamical systems. The objective is to introduce the student to the theory of dynamical systems and its applications. 4 Monday 9:00am-12:00pm & virtual seminar Tuesday 1pm-2pm (subject to changes) Winter 2024 (to be determined)
Gauthier Gidel EN IFT6164 – Adversarial ML (previously named IFT 6756 – Game Theory and ML course) This course is at the interface between game theory, optimization, and machine learning. It tries to understand how to learn models to play games. It will start with some quick notions of game theory to eventually delve into machine learning problems with game formulations such as GANs or Multi-agent RL. This course will also cover the optimization (a.k.a training) of such machine learning games. 4 Tuesday 1:30-3:30pm

Thursday 1:30-3:30pm

January 11, 2024
Jian Tang EN MATH 80600A – Machine Learning II: Deep Learning and Applications This course aims to introduce the basic techniques of deep learning including feedforward neural networks, convolutional neural networks, and recurrent neural networks. Winter 2024 (to be determined) HEC
Doina Precup EN COMP 579 Reinforcement Learning Computer Science (Sci) : Bandit algorithms, finite Markov decision processes, dynamic programming, Monte-Carlo Methods, temporal-difference learning, bootstrapping, planning, approximation methods, on versus off policy learning, policy gradient methods temporal abstraction and inverse reinforcement learning. 4 Winter 2024 (to be determined)
Aditya Mahajan EN ECSE 506 – Stochastic Control and Decision Theory Modeling of stochastic control systems, controlled Markov processes, dynamic programming, imperfect and delayed observations, linear quadratic and Gaussian (LQG) systems, team theory, information structures, static and dynamic teams, dynamic programming for teams, multi-armed bandits. 3 Tuesday and Thursday 10:00-11:30am Winter 2024 (to be determined)
Timothy J. O’Donnell English LING 645: Computational Linguistics Introduction to foundational ideas in computational linguistics and
natural language processing. Topics include formal language theory,
probability theory, estimation and inference, and recursively defined
models of language structure.
Winter 2024 (to be determined)
Bang Liu EN IFT6010 – Modern Natural Language Processing In this course, students will gain a thorough introduction to the basics of NLP, as well as cutting-edge research in Deep Learning for NLP. We will focus on modern techniques for NLP, as well as introduce the applications in our daily lives. Winter 2024 (to be determined)
Aishwarya Agrawal EN IFT 6765 – Links between Computer Vision and Language

A seminar course on recent advances in research problems at the intersection of computer vision and natural language processing, such as caption based image retrieval, grounding referring expressions, image captioning, visual question answering, etc.

4 Monday and Wednesday 2:30-4:30pm January 8, 2024 April 15, 2024 Mila (preferred)
Pierre-Luc Bacon FR IFT6390 – Fundamentals of machine learning Basic elements of statistical learning algorithms. Examples of applications in data mining, nonlinear regression, and temporal data, and deep learning. 4 Winter 2024 (to be determined)
Dhanya Sridhar EN IFT 6168 – Causal inference and machine learning This course combines lectures and seminar-style discussions to cover the foundations of causality and topics like causal representation learning, causal structure discovery, causal abstraction (and its use in understanding large models). 4 Monday and Wednesday 12:30-2:30pm January 8, 2024 April 15, 2024 Mila
Glen Berseth EN IFT 6163 – Robot Learning This class aims to discuss these limitations and study methods to overcome them and enable agents capable of training autonomously, becoming learning and adapting systems that require little supervision. 4 Winter 2024 (to be determined) UdeM
David Rolnick EN COMP 611 – Mathematical Tools for Computer Science This course provides a deep dive into essential mathematics for computer science and is designed to teach not just important mathematical tools but the skill of mathematical thought in the context of CS, including how to write advanced mathematical proofs. 4 Tuesday and Thursday 8:30-10am Winter 2024 (to be determined) TBD
Derek Nowrouzezahrai EN ECSE 446/546 – Realistic/Advanced Image Synthesis This course presents modern mathematical models of lighting and the algorithms needed to solve them and generate beautiful realistic images. Both traditional numerical methods and modern machine learning-based approaches will be covered. 4 Monday and Wednesday Winter 2024 (to be determined) McGill
Siva Reddy, Morgan Sonderegger English COMP 345 / LING 345 From Natural Language to Data Science This course is for people with no experience is NLP and would like to see how it can be used for exciting data science applications. 3 Thursday 10:05-11:25am Winter 2024 (to be determined)
Siva Reddy English COMP 545 / LING 484/782 Natural Language Understanding with Deep Learning / Computational Semantics Throughout the course, we take several concepts in NLU such as meaning or applications such as question answering, and study how the paradigm has shifted, what we gained with each paradigm shift, and what we lost. This course will also delve into how large language models like ChatGPT are built, and latest advances on how to train/use these models for the task at hand. 4 Thursday 1:05-2:25pm Winter 2024 (to be determined)
Siamak Ravanbakhsh EN COMP 588 Probabilistic Graphical Models The course covers representation, inference and learning with graphical models; the topics at high level include directed and undirected graphical models; exact inference; approximate inference using deterministic optimization based methods, as well as stochastic sampling based methods; learning with complete and partial observations. Thursday 10-11:30am Winter 2024 (to be determined) McGill
Golnoosh Farnadi English Responsible AI This is a graduate course, in which we will cover methods for trustworthy and ethical machine learning and AI, focusing on the technical perspective of methods that allow addressing current ethical issues. This course will bring analytic and technical precision to normative debates about the role that data science, machine learning, and artificial intelligence play in consequential decision-making in commerce, employment, finance, healthcare, education, policing, and other areas. 4 TBD Winter 2024 (to be determined) McGill
Prakash Panangaden EN COMP 599 Mathematical Methods The aim of this course is to introduce some continuous mathematics that has become increasingly important in machine learning. 3 Monday and Wednesday 17:35-18:55pm Winter 2024 (to be determined) McGill
Jackie Chi Kit Cheung EN COMP 767 – Formal and Neural Models of Pragmatics In this course, we will examine computational models of pragmatics and how NLP systems have been empirically evaluated for their pragmatic reasoning ability. We will discuss classical theories of formal semantics and pragmatics, as well as more recent statistical and neural models. 3 Thursday 11:30am-1:00pm Winter 2024 (to be determined) McGill