Courses and schedules  Fall 2022 (preliminary list)
See the full list of DIRO courses
Professor  Course/sigles  Description  Credits  Schedule  Dates 

Simon LacosteJulien  IFT 6269 – Probabilistic Graphical Models  System Representation as probabilistic graphical models, inference in graphical models, learning parameters from data.  4  Tue: 4:306:30 pm Fri: 35 pm  06092022 09092022 Mila 
Ioannis Mitliagkas  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  TBD  TBD 
Sarath Chandar  INF8953DE – 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.  3  TBD  TBD 
Laurent Charlin  MATH 80629 Apprentissage automatique I : Analyse des Mégadonnées et Prise de décision  In this course, we will study machine learning models including models for decision making. Massive datasets are now common and require scalable analysis tools. Machine learning provides such tools and is widely used for modelling problems across many fields including artificial intelligence, bioinformatics, finance, marketing, education, transportation, and health. In this context, we study how standard machine learning models for supervised (classification, regression) and unsupervised learning (for example, clustering and topic modelling) can be scaled to massive datasets using modern I am teaching both the English and the French version in Fall 2022.  3  [French] We: 8:30 – 11:30 [English] Mon: 8:30 – 11:30  [French] 310822 [English] 290822HEC 
Jackie C. K. Cheung  COMP 550 – Natural Language Processing  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. 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.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  TBD  TBD

Timothy J. O’Donnell  COMP598/LING 682 – Probabilistic Programming  Probabilistic inference viewed as a form of nonstandard interpretation of programming languages with a focus on sampling algorithms using the programming language Gen.  3  TBD  TBD

Reihaney Rabbany  COMP 596 – 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 coauthorships 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  TBD  TBD

Guy Wolf  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  Mon: 1:305:20 pm  TBD UdeM : 4186 Pav. AndreAisenstadt 
Sarath Chandar  INF8953CE – 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  TBD  TBD 
PierreLuc Bacon  IFT 6760C – Reinforcement Learning  Advanced course in reinforcement learning. Topics: Policy gradient methods, gradient estimation, analysis of valuedbased function approximation methods, optimal control and automatic differentiation, bilevel optimization in metalearning and inverse reinforcement learning.  4  NOT OFFERED THIS FALL 2022 SEMESTER. BACK FOLLOWING YEAR  
Gauthier Gidel and Glen Berseth  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  Tue: 11:30 – 12:30 pm Thu: 4:30 – 6:30 pmLabo – Ma : 12:30 – 2:30 pm  TBD Online 
Golnoosh Farnadi  MATH80630 – Trustworthy Machine Learning  This course will teach students to recognize where and understand why ethical issues and policy questions can arise when applying data science to real world problems. It will focus on ways to conceptualize, measure, and mitigate bias in datadriven decisionmaking. 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. Recent years have shown that unintended discrimination arises naturally and frequently in the use of machine learning and algorithmic decision making. We will work systematically towards a technical understanding of this problem mindful of its social and legal context. 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 decisionmaking in commerce, employment, finance, healthcare, education, policing, and other areas. Students will learn to think critically about how to plan, execute, and evaluate a project with these concerns in mind, and how to cope with novel challenges for which there are often no easy answers or established solutions.
 3  Fri: 3:30 – 6:30 pm  27082022 
Aishwarya Agrawal  IFT6135 – 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 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 networktype 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 networkbase generative models such as Generative Adversarial Networks and Variational Autoencoders.  4  Tue: 9.30 – 11.30am Fri: 12.30 – 2.30pm  06092022 Mila Agora 
Prakash Panangaden and Joey Bose  COMP760: Geometry and Generative Models  In recent years Deep Generative Models have seen remarkable success over a variety of data domains such as images, text, and audio to name a few. However, the predominant approach in many of these models (e.g. GANS, VAE, Normalizing Flows) is to treat data as fixeddimensional continuous vectors in some Euclidean space, despite significant evidence to the contrary (e.g. 3D molecules). This course places a direct emphasis on learning generative models for complex geometries described via manifolds, such as spheres, tori, hyperbolic spaces, implicit surfaces, and homogeneous spaces. The purpose of this seminar course is to understand the key design principles that underpin the new wave of geometryaware generative models that treat the rich geometric structure in data as a firstclass citizen. This seminar course will also serve to develop extensions to these approaches at the leading edge of research and as a result, a major component of the course will focus on class participation through presenting papers and a thematicallyrelevant course project.  3  Fri: 1:00 – 4pm  Mila Auditorium 1 
Courses and schedules  Winter 2023 (preliminary list)
See the full list of DIRO courses
Professor  Course  Description  Credits  Schedule  Dates 

Simon LacosteJulien  IFT 6132 – Advanced Structured Prediction and Optimization  Structured prediction is the problem of learning a prediction mapping between inputs and structured outputs, i.e. outputs that are made of interrelated parts often subject to constraints. Examples include predicting trees, orderings, alignments, etc., and appear in many applications from computer vision, natural language processing and computational biology among others. This is an advanced machine learning course that will focus on the fundamental principles and related tools for structured prediction. The course will review the state of the art, tie older and newer approaches together, as well as identify open questions. It will consist of a mix of faculty lectures, class discussions and paper presentations by students, as well as a research project. Prerequisite: I will assume that most of the content of IFT 6269 Probabilistic Graphical Models is known by the students.  4  TBD  TBD 
Aaron Courville  IFT6135 – 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 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 networktype 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 networkbase generative models such as Generative Adversarial Networks and Variational Autoencoders. This course is available in English and French.  4  TBD  TBD 
Ioannis Mitliagkas  IFT 6085 – Theoretical principles for deep learning
 Research in deep learning produces stateoftheart results on a number of machine learning tasks. Most of those advances are driven by intuition and massive exploration through trial and error. As a result, theory is currently lagging behind practice. The ML community does not fully understand why the best methods work. Why can we reliably optimize nonconvex objectives? In this class we will go over a number of recent publications that attempt to shed light onto these questions. Before discussing the new results in each paper we will first introduce the necessary fundamental tools from optimization, statistics, information theory and statistical mechanics. The purpose of this class is to get students engaged with new research in the area. To that end, the majority of credit will be given for a class project report and presentation on a relevant topic. Note: This is an advanced class designed for PhD students with serious mathematical background.  4  TBD  TBD 
Irina Rish  IFT 6167 (6760B) – Neural Scaling Laws and Foundation Models  This seminarstyle course will focus on recent advances in the rapidly developing area of “foundation models”, i.e. largescale neural network models (e.g., GPT3, CLIP, DALLe, etc) pretrained on very large, diverse datasets. Such models often demonstrate significant improvement in their fewshot generalization abilities, as compared to their smallerscale counterparts, across a wide range of downstream tasks – what one could call a “transformation of quantity into quality” or an “emergent behavior”. This is an important step towards a longstanding objective of achieving Artificial General Intelligence (AGI). By AGI here we mean literally a “general”, i.e. broad, versatile AI capable of quickly adapting to a wide range of situations and tasks, both novel and those encountered before – i.e. achieving a good stability (memory) vs plasticity (adaptation) tradeoff, using the continual learning terminology. In this course, we will survey most recent advances in largescale pretrained models, focusing specifically on empirical scaling laws of such systems’ performance, with increasing compute, model size, and pretraining data (power laws, phase transitions). We will also explore the tradeoff between the increasing AI capabilities and AI safety/alignment with human values, considering a range of evaluation metrics beyond the predictive performance. Finally, we will touch upon several related fields, including transfer, continual and metalearning, as well as outofdistribution generalization, robustness and invariant/causal predictive modeling.  4  TBD  TBD 
Guillaume Rabusseau  IFT 6760A – 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. – Fundamental notions of linear and multilinear algebra. – Old and new ML methods leveraging matrix and tensor decomposition: PCA/CCA, collaborative filtering, spectral graph clustering, spectral methods for HMM, KFAC, spectral normalization, tensor method of moments, NN/MRF compression, tensor regression/completion, etc. – Open problems. 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. – Fundamental notions of linear and multilinear algebra. – Old and new ML methods leveraging matrix and tensor decomposition: PCA/CCA, collaborative filtering, spectral graph clustering, spectral methods for HMM, KFAC, spectral normalization, tensor method of moments, NN/MRF compression, tensor regression/completion, etc. – Open problems.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. – Fundamental notions of linear and multilinear algebra. – Old and new ML methods leveraging matrix and tensor decomposition: PCA/CCA, collaborative filtering, spectral graph clustering, spectral methods for HMM, KFAC, spectral normalization, tensor method of moments, NN/MRF compression, tensor regression/completion, etc. – Open problems.  4  TBD  TBD 
Guillaume Lajoie  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. Firstly, classical dynamics analysis techniques will be presented: continuous and discrete flows, existence and stability of solutions, invariant manifolds, bifurcations and normal forms. Secondly, an introduction to ergodic theory will be presented: chaotic dynamics, strange attractors, dynamic entropy, highdimensional systems (e.g. networks), driven dynamics and information processing. Particular attention will be paid to computations performed by dynamical systems. Throughout the course, there will be an emphasis on modern applications in neuroscience, artificial intelligence, and datadriven modeling. This inlcudes: dynamical systems tools for optimization, network dynamics and links to deep learning & representation theory, computational neuroscience tools. At the end of the course, the student will be able to apply dynamical systems analysis techniques to concrete problems, as well as navigate the modern dynamical systems literature. Several examples and applications making use of numerical simulations will be used. To take this course, the student must master, at an undergraduate level, notions of calculus, linear differential equations, linear algebra and probability.  4  Mon: 9:00 am – 12:00pm & virtual seminar Tue: 1:00 – 2:00 pm (subject to changes)  TBD 
Blake Richards  COMP 549 – Braininspired AI (Replaces COMP596)  This is a historical overview of the influence of neuroscience on artificial intelligence. It will be a seminar style class, mixing lecture, discussion, and class presentations. Topic covered will include perceptrons, the origins of reinforcement learning, parallel distributed processing, Boltzmann machines, brain inspired neural network architectures, and modern approaches to deep learning that incorporate attention, memory and ensembles. This is a historical overview of the influence of neuroscience on artificial intelligence. It will be a seminar style class, mixing lecture, discussion, and class presentations. Topic covered will include perceptrons, the origins of reinforcement learning, parallel distributed processing, Boltzmann machines, brain inspired neural network architectures, and modern approaches to deep learning that incorporate attention, memory and ensembles.This is a historical overview of the influence of neuroscience on artificial intelligence. It will be a seminar style class, mixing lecture, discussion, and class presentations. Topic covered will include perceptrons, the origins of reinforcement learning, parallel distributed processing, Boltzmann machines, brain inspired neural network architectures, and modern approaches to deep learning that incorporate attention, memory and ensembles.  3  TBD  TBD 
Gauthier Gidel  IFT 6756 – Game Theory and ML course (The number of the course and the name will change. New Name: Adversarial ML, new number IF6164)  The number of Machine Learning applications related to game theory has been growing in the last couple of years. For example, twoplayer zerosum games are important for generative modeling (GANs) and mastering games like Go or Poker via selfplay. 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 Multiagent RL. This course will also cover the optimization (a.k.a training) of such machine learning games.  4  TBD  TBD 
Jian Tang  MATH 80600A – Machine Learning II: Deep Learning and Applications  Deep learning has achieved great success in a variety of fields such as speech recognition, image understanding, and natural language understanding. This course aims to introduce the basic techniques of deep learning and recent progress of deep learning on natural language understanding and graph analysis. This course aims to introduce the basic techniques of deep learning including feedforward neural networks, convolutional neural networks, and recurrent neural networks. We will also cover recent progress on deep generative models. Finally, we will introduce how to apply these techniques to natural language understanding and graph analysis.  3  TBD  TBD 
Doina Precup  COMP 579 Reinforcement Learning  Computer Science (Sci) : Bandit algorithms, finite Markov decision processes, dynamic programming, MonteCarlo Methods, temporaldifference learning, bootstrapping, planning, approximation methods, on versus off policy learning, policy gradient methods temporal abstraction and inverse reinforcement learning.  4  TBD  TBD 
Reihaneh Rabbany  COMP 551 – Applied Machine Learning  This course covers a selected set of topics in machine learning and data mining, with an emphasis on understanding the inner workings of the common algorithms. The majority of sections are related to commonly used supervised learning techniques, and to a lesser degree unsupervised methods. This includes fundamentals of algorithms on linear and logistic regression, decision trees, support vector machines, clustering, neural networks, as well as key techniques for feature selection and dimensionality reduction, error estimation and empirical validation.  4  TBD  TBD 
Aditya Mahajan  ECSE 506 – Stochastic Control and Decision Theory  Markov decision processes (MDP), dynamic programming and approximate dynamic programming. Stochastic monotonicity, structure of optimal policies. Models with imperfect and delayed observations, partially observable Markov decision processes (POMDPs), information state and approximate information state. Linear quadratic and Gaussian (LQG) systems, team theory, information structures, static and dynamic teams, dynamic programming for teams.  3  TBD  TBD 
Siva Reddy  COMP 599 – Natural Language Understanding with Deep Learning  The field of natural language processing (NLP) has seen multiple paradigm shifts over decades, from symbolic AI to statistical methods to deep learning. We review this shift through the lens of natural language understanding (NLU), a branch of NLP that deals with “meaning”. We start with what is meaning and what does it mean for a machine to understand language? We explore how to represent the meaning of words, phrases, sentences and discourse. We then dive into many useful NLU applications.  TBD  TBD  TBD 
Golnoosh Farnadi  80629A – Machine Learning I: LargeScale Data Analysis and Decision Making  In this course wi will study machine learning models. In addition to standards models, we will study models for analyzing user behaviour and for decision making. Massive datasets are now common and require scalable analysis tools. Machine learning provides such tools and is widely used for modelling problems across many fields including artificial intelligence, bioinformatics, finance, marketing, education, transportation, and health. In this context, we study how standard machine learning models for supervised (classification, regression) and unsupervised learning (for example, clustering and topic modelling) can be scaled to massive datasets using modern computation techniques (for example, computer clusters). In addition, we will discuss recent models for recommender systems as well as for decision making (including multiarm bandits and reinforcement learning).Through a course project students will have the opportunity to gain practical experience with the analysis of datasets from their field(s) of interest. A certain level of familiarity with computer programming will be expected.  3  Fri: 12:30 – 3:30 pm  TBD 
Timothy J. O’Donnell  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. Emphasis on both the mathematical foundations of the field as well as how to use these tools to understand human language.  TBD  TBD  TBD 
Aishwarya Agrawal  IFT6765AA – 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  Tue: 9.30 – 11.30am Fri: 1.30 – 3.30pm  TBD Mila (preferred) 
PierreLuc Bacon (Ioannis in the Fall, PLB in the winter)  IFT6390 – Fundamentals in machine learning  Basic elements of statistical learning algorithms. Examples of applications in data mining, nonlinear regression, and temporal data, and deep learning.  4  Mon: 12302:30 PM Wed: 2:305:30 PM  TBD UdeM 
Dhanya Sridhar  IFT 6251 – Causal inference and machine learning  There is a growing interest in the intersection of causal inference and machine learning. On one hand, ML methods — e.g., prediction methods, unsupervised methods, representation learning — can be adapted to estimate causal relationships between variables. On the other hand, the language of causality could lead to new learning criteria that yield more robust and fair ML algorithms. In this course, we’ll begin with an introduction to the theory behind causal inference. Next, we’ll cover work on causal estimation with neural networks, representation learning for causal inference, and flexible sensitivity analysis. We’ll conclude with work that draws upon causality to make machine learning methods fair or robust. This is an advanced, seminarstyle course and students are expected to have a strong background in ML.  4  TBD  TBD 
Glen Berseth  IFT 6095 – Robot Learning  Learning methods such as deep reinforcement learning have shown success in solving simulated planning and control problems but struggle to produce diverse, intelligent behaviour, on robots. 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. By the end of the course, each student should have a solid grasp of different techniques to train robots to accomplish tasks in the real world. These techniques covered in the course include but are not limited to reinforcement learning, batch RL, multitask RL, modelbased RL, Sim2Real, hierarchical RL, goal conditioned RL, multiAgent RL, the fragility of RL, metalevel decision making and learning reward functions.  TBD  TBD  TBD 
David Rolnick  COMP 767 – Machine learning applied to climate change  This seminar will explore how machine learning can be applied in fighting climate change. We will look at ways that machine learning can be used to help mitigate greenhouse gas emissions and adapt to the effects of climate change – via applications in electricity systems, buildings, transportation, agriculture, disaster response, and many other areas. Particular emphasis will be given to understanding exactly when machine learning is relevant and helpful, and how to go about scoping, developing, and deploying a project so that it has the intended impact.  4  TBD  McGill 
Derek Nowrouzezahrai  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 learningbased approaches will be covered.  4  TBD  McGill 
Siva Reddy, Timothy J. O’Donnell  LING 682 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. We suggest other NLP/CL courses if you want to focus on theoretical side of NLP/CL. Topics covered in this course include: Language data and applications, Searching through data, How to make sense of data, Language Modeling, Language to decisions, Information Retrieval, Information Extraction, Social Networks (Twitter and Facebook data), Recommendation systems, Ethics.  4  TBD  TBD 