Courses

Courses and schedules - Winter 2020 (preliminary list)

 

 

ProfessorCourseDescriptionCreditsScheduleDates
Simon Lacoste-JulienIFT 6132 -Advanced Structures Prediction and OptimizationAdvanced topics for prediction of structured objects (such as graphs, couplings, flow networks). Generative learning vs.
discriminative. Energy models. Conditional random fields. Structured and latent variable SVM, CCCP algorithms. Large-scale optimization: Frank-Wolfe, reduced variance SGD, block-coordinate methods. Learn to search. NNI. algorithms
combinatorics: cost-min networks, sub-modular optimization,
dynamic programming.
4Tue. 2:00 – 4:00
Thu.1:30 – 3:30
08-01-2018

12-04-2019

Aaron CourvilleIFT 6135 – Apprentissage de représentations (Winter 2019)Advanced topics in learning algorithms: deep architectures, neural networks and unsupervised probabilistic models.

https://sites.google.com/mila.quebec/ift6135

4Mon. 9:30 – 11:30
Wed. 12:30 – 2:30
 07-01-2019

10-04-2019

Ioannis MitliagkasIFT-6085 – Theoretical principles for deep learningResearch in deep learning produces state-of-the-art 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 non-convex objectives? How expressive are our architectures, in terms of the hypothesis class they describe? Why do some of our most complex models generalize to unseen examples when we use datasets orders of magnitude smaller than what the classic statistical learning theory deems sufficient? A symptom of this lack of understanding is that deep learning methods largely lack guarantees and interpretability, two necessary properties for mission-critical applications. More importantly, a solid theoretical foundation can aid the design of a new generation of efficient methods—sans the need for blind trial-and-error-based exploration. 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.4Wed.  9:30 – 11:30

Thu. 9:00 – 11:00

08-01-2020

16-04-2020

Guillaume RabusseauIFT-6760a – Séminaire en apprentissage automatiqueThis course will give an overview of linear/multilinear algebra techniques for designing/analyzing ML algorithms and models, and engage students with new research in the area.
– Fundamental notions of linear and multilinear algebra.
– ML methods leveraging matrix/tensor decomposition: PCA/CCA, collaborative filtering, spectral graph clustering, spectral methods for HMM, K-FAC, spectral normalization, tensor method of moments, NN/MRF compression, tensor completion, etc.
– Open problems.
4Tue. 12:30 – 2:30

Thu. 11:30 – 1:30

07-01-2019

16-04-2019

Irina RishIFT 6760b – Continual Learning: Towards “Broad” AIStephen Hawking famously said, ‘Intelligence is the ability to adapt to change.’ While today’s AI systems can achieve impressive performance in specific tasks, from accurate image recognition to super-human performance in games such as Go and chess, they are still quite “narrow”, i.e. not being able to easily adapt to a wide range of new tasks and environments, without forgetting what they have learned before – something that humans and animals seem to do naturally during their lifetime. This course will focus on the rapidly growing research area of machine learning called continual lifelong learning which aims to push modern AI from “narrow” to “broad”, i.e. to develop learning models and algorithms capable of never-ending, lifelong, continual learning over a large, and potentially infinite set of drastically different tasks and environments. In this course, we will review state-of-art literature on continual lifelong learning in modern AI, including catastrophic forgetting problem and recent approaches to overcoming it in deep neural networks, from augmenting stochastic gradient decent algorithm to alternative optimization approaches, architecture adaptation/evolution based on expansion/compression, dynamic routing/selective execution (“internal” attention) and other approaches; moreover, we will also survey related work on stability vs plasticity dilemma in neuroscience and related topics in biology of adaptation and memory.4Mon. 3:00 – 5:30

Thu. 4:30 – 6:30

02-02-2020

01-06-2020

Pierre-Luc BaconIFT 6760C – Reinforcement LearningAdvanced 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.4Tue. 9:30 – 11:30
Fri. 1:30 – 3:30
TBD
Aaron CourvilleIFT6759 – Professional MasterPreparation for practical applications of machine learning through concrete projects on real data use of specialized machine learning software for artificial intelligence Note: It is recommended to take IFT6135 Representation Learning before or concomitant.4Wed. 2:30 – 4:30

Fri. 9:30 – 11:30

TBD
Doina PrecupCOMP 767 – Advanced Topics: Applications 2Computer Science (Sci) : Advanced topics in computing systems.4Mon. 8:30-10:00

Wed. 8:30 – 10:00

TBD
Guillaume LajoieMAT6115 Dynamical SystemsIntroduction to nonlinear differential equations, classical techniques for dynamics analysis, discrete and continuous flows, existence and stability of solutions, invariant manifolds, bifurcations and normal forms, ergodic theory and chaos. The course will focus on modern applications of Dynamical Systems theory, including: optimization dynamics, recurrent network dynamics, computational neuroscience.3Tue. 9:30 – 12:30

07-01-2020

28-04-2020

Blake RichardsCOMP 596 – Topics in Computer Science 3
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.
3Tue. 1:00 – 2:30

Thu. 1:00 – 2:30

 07-01-2020

9-04-2020

Siva Reddy COMP 764 Advanced Topics Systems 1
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.

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? We will critically evaluate existing ideas and try to come up with new ideas that challenge existing limitations. We will particularly work on making deep learning models for language more robust.

This is a seminar-style course, where the class as a whole will work together in running the course. In the first few lectures, I will provide an overview of NLU and highlight the challenges the field is facing.

4Tue. 4:05 – 5:25

Thu. 4:05 – 5:25

 TBD
Emma FrejingerIFT-6521 – Programmation dynamique (Winter 2019)This course aims to study algorithms and mathematical tools used to analyze or optimize sequential decision processes, for which there is interaction between decisions, in a certain or uncertain environment. We will see how to implement the calculation algorithms and we will study their performance. In several cases we will be able to demonstrate certain properties characterizing the form of an optimal policy. Examples of applications from different areas, including management and finance, will also be examined.4Mon. 1:30-3:30

Thu. 11:30-1:30

n/a
Jian Tang 6-600-18A – Data Mining Techniques (Winter 2019) This course aims to introduce the basic techniques of data mining and their applications to different types of real-world data including transaction data, text data, and set data.3Fri. 6:30 – 9:3011-01-2019

12-04-2019

Bernard GendronIFT 6551 – Programmation en nombres entiers (Winter 2019)Ce cours vise, d’une part, à introduire l’étudiant à de nombreux problèmes d’optimisation pouvant être formulés à l’aide de variables entières et, d’autre part, à le familiariser avec les méthodes de la programmation en nombres entiers. Pour plus de détails, consultez le texte suivant, qui propose une synthèse des sujets traités.4Mon. 11:30 – 1:30

Tue. 10:30 – 12:30

 

Courses and schedules - Fall 2019

Noms ProfesseursCours/siglesDescriptionsCréditsHorairesDates
SiMon. Lacoste-JulienIFT 6269 – Modèles Graphiques probabilistes et apprentissageSystem Representation as probabilistic graphical models, inference in graphical models, learning parameters from data.4Tue. 2:30 – 4:30
Fri. 1:30 – 3:30
03-09-2019

06-12-2019

Ioannis MitliagkasIFT 6390 – Fondements de l’Apprentissage MachineBasic elements of statistical and symbolic learning algorithms. Examples of applications in data mining, pattern recognition, nonlinear regression, and time data.4Section A :Wed. 9:30-11:30  et Thu. 9:30-10:30

Section A1 Thu. 10:30-12:30

Section A102 Thu. 10:30-12:30

03-09-2019

05-12-2019

Laurent CharlinMATH 80629A Machine Learning l : Large-Scale Data Analysis and Decision Making In this course, we will study models of machine learning. Furthermore, we will also study models of user behavior analysis and decision making. Large datasets are now comMon. and require scalable analytics. In addition, we will discuss recent models for referral systems as well as for decision-making (including multi-armed bandits and reinforcement learning).3Wed. 8:30 – 11:3004-09-2019

04-12-2019

Laurent CharlinMATH 80629 Apprentissage automatique I : Analyse des Mégadonnées et Prise de décisionIn this course, we will study models of machine learning. Furthermore, we will also study models of user behavior analysis and decision making. Large datasets are now comMon. and require scalable analytics. In addition, we will discuss recent models for referral systems as well as for decision-making (including multi-armed bandits and reinforcement learning).3Thu. 8:30 – 11:3005-09-2019

05-12-2019

Jackie C. K. CheungCOMP 550 – Natural Language ProcessingAn 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.3Tue. 11:35 – 12:55

Thu. 11:35 – 12:55

03-09-2019

-10-12-2019

William L. HamiltonCOMP 551- Applied Machine LearningThe course will cover selected topics and new developments in data mining and applied machine learning, with a particular emphasis on good methods and practices for effective deployment of real systems. We will study commonly used algorithms and techniques, including linear and logistic regression, clustering, neural networks, support vector machines, decision trees and more. We will also discuss methods to address practical issues such as empirical validation, feature selection, dimensionality reduction, and error estimation.3Mon. 4:00 – 5:30

Wed. 4:00 – 5:30

06-09-2019

06-12-2019

Rabbany ReihaneyCOMP 596 – Network ScienceNetworks 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.3Tue. 10:00 – 11:30

Thu. 10:00 – 11:30

03-09-2019

09-12-2019

Liam PaullIFT 6757 – Véhicule Autonomes
3Mon. 10:30 – 12:30

Wed. 11:30 – 1:30

02-09-2019

04-12-2019

Guy WolfMAT 6480W / STT 6705V – Special Topics in Geometric Data Analysis
3Wed. 3:30 – 5:00

Thu.3:30 – 5:00

04-09-2019

04-12-2019

Courses and schedules - Winter 2019

 

 

ProfessorCourseDescriptionCreditsScheduleDates
Lacoste-Julien SimonIFT 6132 -Advanced Structures Prediction and Optimization
Advanced topics for prediction of structured objects (such as graphs, couplings, flow networks). Generative learning vs.
discriminative. Energy models. Conditional random fields. Structured and latent variable SVM, CCCP algorithms. Large-scale optimization: Frank-Wolfe, reduced variance SGD, block-coordinate methods. Learn to search. NNI. algorithms
combinatorics: cost-min networks, sub-modular optimization,
dynamic programming.
4Tue. 2:30 – 14:30
Thu. 1:30 – 3:30
08-01-2018

12-04-2019

Courville AaronIFT 6135 – Apprentissage de représentationsAdvanced topics in learning algorithms: deep architectures, neural networks and unsupervised probabilistic models.

https://sites.google.com/mila.quebec/ift6135

4Mon. 9:30 – 11:30
Wed. 12:30 – 2:30
 07-01-2019

10-04-2019

Mitliagkas IoannisIFT-6085 – Theoretical principles for deep learningThis course consists of an overview of the most recent theoretical publications on deep learning. For each selected publication, Dr. Mitliagkas will introduce the theoretical concepts necessary to understand the scientific approach of the authors, but also the results presented. Several topics will be covered, such as the optimization of nonconvex functions and information theory. Since the main objective of this course is to introduce participants to new research in this area, the main evaluation of this course will be in the form of a research project.4Wed.  9:30 – 11:30

Thu. 9:00 – 11:30

09-01-2019

11-04-2019

Rabusseau GuillaumeIFT-6760a – Séminaire en apprentissage automatiqueThis course will give an overview of linear/multilinear algebra techniques for designing/analyzing ML algorithms and models, and engage students with new research in the area.
– Fundamental notions of linear and multilinear algebra.
– ML methods leveraging matrix/tensor decomposition: PCA/CCA, collaborative filtering, spectral graph clustering, spectral methods for HMM, K-FAC, spectral normalization, tensor method of moments, NN/MRF compression, tensor completion, etc.
– Open problems.
4Tue. 12:30 – 2:30

Thu. 11:30 – 1:30

08-01-2019

12-04-2019

Frejinger EmmaIFT-6521 – Programmation dynamiqueThis course aims to study algorithms and mathematical tools used to analyze or optimize sequential decision processes, for which there is interaction between decisions, in a certain or uncertain environment. We will see how to implement the calculation algorithms and we will study their performance. In several cases we will be able to demonstrate certain properties characterizing the form of an optimal policy. Examples of applications from different areas, including management and finance, will also be examined.4Mon. 1:30-3:30

Thu. 11:30-1:30

n/a
Tang Jian 6-600-18A – Data Mining Techniques This course aims to introduce the basic techniques of data mining and their applications to different types of real-world data including transaction data, text data, and set data.3Fri. 6:30 – 9:3011-01-2019

12-04-2019

Gendron BernardIFT 6551 – Programmation en nombres entiersCe cours vise, d’une part, à introduire l’étudiant à de nombreux problèmes d’optimisation pouvant être formulés à l’aide de variables entières et, d’autre part, à le familiariser avec les méthodes de la programmation en nombres entiers. Pour plus de détails, consultez le texte suivant, qui propose une synthèse des sujets traités.4Mon. 11:30 – 1:30

Tue. 10:30 – 12:30

 

Courses and schedules - Fall 2018

Professor NamesCourseDescriptionsCreditsHoursDates
Ioannis MitliagkasIFT 6390 – Fundamentals of Machine Learning (Graduate)This course is given in English. Basics of statistical and symbolic learning algorithms. Examples of applications in data mining, pattern recognition, nonlinear regression, and temporal data.4Wed 9:30 – 11:30 Lect
Th 9:30 – 10:30 Lect
Th 10:30 – 12:30 Lab
2018-09-05

2018-12-06

Lacoste-Julien SimonIFT 6269 – Probabilistic graphical models and learningRepresenting systems as graphical probabilistic models, inference in graphical models, learning parameters from data.4Tue 14:30 – 16:30 Sem
Fri 13:30 – 15:30 Sem
2018-09-04 

2018-12-07

Paull Liam IFT 6757-Autonomous VehiclesProblems in perception, navigation, and control, and their systems-level integration in the context of self-driving vehicles through an open-source curriculum for autonomy education (Duckietown) that emphasizes hands-on experience4Mon. 10:30 – 12:30
Wed. 11:30 – 1:30
05/09/2018

05/12/2018

Guillaume RabusseauIFT3395 Fundamentals of Machine Learning (Under Graduate)This course is given in French. Basics of statistical and symbolic learning algorithms. Examples of applications in data mining, pattern recognition, nonlinear regression, and temporal data.3Wed 9:30 – 11:30 Lect
Th 9:30 – 10:30 Lect
Th 10:30 – 12:30 Lab
2018-09-05

2018-12-06

Cheung JackieComp 550 (McGill)- Natural Language ProcessingAn 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.32:35 PM – 3:55 PM TR

ENGMC 13

n/a
Lodi AndreaMTH6404 (Poly)- Integer ProgrammingCourse is given in English in Fall and French in Winter. Branch-and-bound: enumeration trees, exploration strategy, branching rules. Polyhedral theory: valid inequalities, dimensions. Unimodularity. Method of intersecting planes. Chvátal-Gomory cuts. Dantzig-Wolfe, column generation, Benders decompositions. Lagrangian relaxation. Backpack and courrier problems.3n/an/a
Charlin Laurent6-602-07 (HEC)- Applied multidimensional analysisData mining, factorial analysis, selecting variables and models, logistic regression, clustering, survival analysis, missing data3Mon. 12:00-15:00 Lectn/a
 Charlin Laurent80-629-17A (HEC) – Machine Learning for Large-Scale Data Analysis & Decision MakingMachine learning models for supervised (classification, regression) and unsupervised learning (for example, clustering and topic modelling) scaled to massive datasets using modern computation techniques (for example, computer clusters).3Wed 8:30-11:30 Lectn/a
Alain TappData Science (IFT3700/IFT6700)Contextualization and applications of probabilities, statistics, optimization and computer tools for data science; cleaning and visualization of data; statistical issues of machine learning on structured data.n/aMon. 9:30 – 10:30

Tue. 12:30-14:30

Mon. 10:30-12:30

9-10-2018

03-12-2018

Guillaume LajoieDynamical Systems (MAT6115)Introduction to the treatment of nonlinear differential equations, classical dynamics analysis techniques, continuous and discrete flows, existence and stability of solutions, invariant manifolds, bifurcations and normal forms, an introduction to ergodic theory and an overview of modern applications.3Tue. 10:30-12:00

Thu. 10:30-12:00

04/09/2018

20/12/2018

Courses and schedules - Winter 2018

Noms ProfesseursCours/siglesDescriptionsCréditsHorairesDates
Lacoste-Julien SimonIFT 6132 – Advanced Structured Prediction and OptimizationAdvanced topics for prediction of structured objects (such as:
graphs, matching, network flows). Gernative vs. discriminative
learning continuum. Energy-based models & surrogate losses.
Conditional random field (CRF). Structured SVM. Latent variable
structured SVM, CCCP algorithm. Large-scale optimization: Frank-Wolfe, variance-reduced SGD, block-coordinate methods. Learning to search. RNN. Combinatorial algorithms: min-cost network flow, submodular optimization, dynamic programs.
4Tue. 14:30 – 17:30
Fri. 14:30 – 16:30
23-01-2018 – 30-04-2018
Courville AaronIFT 6135 – Learning RepresentationsAdvanced subjects in learning algorithms: deep architectures, neural networks and non-supervised probabilistic models.
4Mon. 09:30 – 11:30
Wed. 12:30 – 14:30
 08-01-2018 – 30-04-2018
Pal ChristopherINF 8225 (Poly) – Probabilistic techniques and learningProbabilistic methods in artificial intelligence. Bayesian networks, hidden Markov models, random Markov fields. Inference. Theory of statistical decisions and networks. Probabilistic treatment of natural language and visual perception. Recommendation systems, data mining, information search and computer vision. Deep learning and reinforcement learning.3Tue. 15:45 – 18:45
Thu. 13:45 – 16:45
08-01-2018 – 30-04-2018
Precup DoinaCOMP 767 (McGill) – Advanced Topics: Reinforcement learningAdvanced topics in reinforcement learning.4Tue. 4:00 – 5:30 PM
Thu. 4:00 – 5:30 PM
08-01-2018 – 16-04-2018
Pineau JoelleCOMP 551 (McGill) – Applied Machine LearningClustering, neural networks, support vector machines, decision trees. Feature selection and dimensionality reduction, error estimation and empirical validation, algorithm design and parallelization, and handling of large data sets.4Tue. 11:35 AM – 12:55 PM
Thu. 11:35 AM – 12:55 PM
Mon. 8:35 AM – 9:55 AM
Wed. : 8:35 AM – 9:55 AM
08-01-2018 – 16-04-2018
Ioannis MitliagkasIFT-6085 – Theoretical principles for deep learningOverview of the most recent publications in the field of deep learning. Before discussing the new results in each paper, Dr. Mitliagkas 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.4Wed. 9:30 – 11:30
Thu. 9:00 – 11h00
 08-01-2018 – 30-04-2018
Alain TappIFT 6271 – Sécurité informatiqueConfidentiality and integrity of data with public and private key. Protection of TCP/IP layers; protection against computer parasites. User authentification methods. Evaluating and managing risks.3Mon. 09:30 – 11:30
Thu. 12:30 – 13:30
 08-01-2018 – 30-04-2018
Charlin Laurentn/an/an/an/an/a
Bengio Yoshuan/an/an/an/an/a
Vincent Pascaln/an/an/an/an/a

 

Courses and schedules - Fall 2017

Noms ProfesseursCours/siglesDescriptionsCréditsHorairesDates
Courville AaronIFT 3395/6390 – Fondamentals of Machine Learning
Basics of statistical and symbolic learning algorithms. Applications in data mining, pattern recognition, non-linear regression, time series data.4Wed. 9:30 – 11:30 Lect
Thu. 9:30 – 10:30 Lect
Thu. 10:30 – 12:30 Lab
2017-09-06 – 2017-12-07
Lacoste-Julien SimonIFT 6269 – Probabilistic graphical models and learningRepresenting systems as graphical probabilistic models, inference in graphical models, learning parameters from data.4Tue. 14:30 – 16:30 Sem
Fri. 13:30 – 15:30 Sem
2017-09-05 – 2017-12-08
Paull LiamIFT 6080 – DuckietownProblems in perception, navigation, and control, and their systems-level integration in the context of self-driving vehicles through an open-source curriculum for autonomy education that emphasizes hands-on experience.4mon. 10:30 – 12:20 Cours
Wed. 11:30-13:30 Cours
2017-09-06 – 2017-12-06
Pal ChristopherINF8702 (Poly) – Advanced Computer GraphicsReal-time rendering, polygonal and surface rendering. Textures. Parametric curves and surfaces. Local reflexion models. Illumination models. Volume rendering. Artistic rendering and virtual reality.3Tue. 9:30-12:30 Lab
Fri. 12:45 – 15:45 Lect
Precup DoinaCOMP 652 (McGill) – Machine LearningAn overview of state-of-the-art algorithms used in machine learning, including theoretical properties and practical applications of these algorithms.4Mon. 14:35 – 15:55 Lect
Wed. 14:35 – 15:55 Lect
2017-09-05 – 2017-12-07
Pineau JoelleCOMP 551 (McGill) – Applied Machine LearningClustering, neural networks, support vector machines, decision trees. Feature selection and dimensionality reduction, error estimation and empirical validation, algorithm design and parallelization, and handling of large data sets.4Mon. 13:05 – 14:25 Lect
Wed. 08:35 – 9:55 Lect
2017-09-05 – 2017-12-07
Charlin Laurent6-602-07 (HEC)- Applied multidimensional analysisData mining, factorial analysis, selecting variables and models, logistic regression, grouping analysis, survival analysis, missing data3Thu. 18:45-21:45 Lectn/a
80-629-17A (HEC) – Machine Learning for Large-Scale Data Analysis & Decision MakingMachine learning models for supervised (classification, regression) and unsupervised learning (for example, clustering and topic modelling) scaled to massive datasets using modern computation techniques (for example, computer clusters).3Wed. 8:30-11:30 Lectn/a
Lodi AndreaMTH6404 (Poly)- Integer ProgrammingCourse given in English in Fall and French in Winter. Branch-and-bound: enumeration trees, exploration strategy, branching rules. Polyhedral theory: valid inequalities, dimensions. Unimodularity. Method of intersecting planes. Chvátal-Gomory cuts. Dantzig-Wolfe, column generation, Benders decompositions. Lagrangian relaxation. Backpack and courrier problems.3Tue. 12:45 – 15:45 Lectn/a
Cheung JackieComp 550 (McGill)- Natural Language ProcessingAn 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.3Tue. 16:05 – 17:25 Lect
Thu. 16:05 – 17:25 Lect
2017-09-05 – 2017-12-06

Courses and schedules - Summer 2017

ProfessorCourse/NumberDescriptionsCreditsScheduleDates
Pal ChristopherINF6953H (Poly) – Deep LearningConvolutional neural networks, autoencoders, recurrent neural networks, long short-term memory (LSTM) networks. Stochastic networks, conditional random fields, Boltzmann machines. Mixed stochastic and deterministic models. Deep reinforcement learning.3Tue. 13:00 – 16:00 Lect
Wed. 13:00 – 16:00 Lab
Thu. 13:00 – 16:00 Lect
2017-05-07 – 2017-08-24

Courses and schedules - Winter 2017

ProfessorCourse/NumberDescriptionsCreditsScheduleDates
Lacoste-Julien SimonIFT 6085 – Advanced Structured PredictionEnergy-based models & surrogate losses. Generative learning / discriminative learning continuum. Conditional random field (CRF). Structured SVM. Latent variable structured SVM, CCCP algorithm. Large-scale optimization: Frank-Wolfe, variance-reduced SGD, block-coordinate methods. Learning to search. RNN. Combinatorial algorithms: min-cost network flow, submodular optimization, dynamic programs.4Wed. 10:30 – 12:30
Fri. 15:00 – 16:59
6 Jan to 12 Apr 2017
Courville AaronIFT 6266 – Learning algorithmsAdvanced subjects in learning algorithms: deep architectures, neural networks and non-supervised probabilistic models.4Mon. 14:30 – 16:30
Thu. 09:30 – 11:30
5 Jan to 13 Apr 2017
Pal ChristopherINF 8225 (Poly) – Probabilistic techniques and learningProbabilistic methods in artificial intelligence. Bayesian networks, hidden Markov models, random Markov fields. Inference. Theory of statistical decisions and networks. Probabilistic treatment of natural language and visual perception. Recommendation systems, data mining, information search and computer vision3Mon. 12h45 – 15h45 Lab
Tue. 9h30 – 12h30 Lecture
Precup DoinaCOMP 652 (McGill) – Machine LearningAn overview of state-of-the-art algorithms used in machine learning, including theoretical properties and practical applications of these algorithms.4Tue. 13:05 – 14:25
Thu. 13:05 – 14:25
4 Jan to 11 Apr 2017
COMP 767 (McGill) – Advanced Topics: Reinforcement learningAdvanced topics in reinforcement learning.4Fri. 10:05 – 12:554 Jan to 11 Apr 2017
Pineau JoelleCOMP 551 (McGill) – Applied Machine LearningClustering, neural networks, support vector machines, decision trees. Feature selection and dimensionality reduction, error estimation and empirical validation, algorithm design and parallelization, and handling of large data sets.4Tue. 13:05 – 14:25
Thu. 13:05 – 14:25
4 Jan to 11 Apr 2017
Charlin Laurentn/an/an/an/an/a
Bengio Yoshuan/an/an/an/an/a
Vincent Pascaln/an/an/an/an/a

Courses and schedules - Fall 2016

ProfessorCourse/NumberDescriptionsCreditsScheduleDates
Vincent PascalIFT 3395 – Fundamentals of machine learning (1st cycle)Basic elements of statistical and symbolic learning algorithms. Applications in data mining, pattern recognition, nonlinear regression and time-series data.

Note: Knowledge of numerical analysis recommend, such as in IFT 2425.

3Wed. 09:30 – 11:30 Lecture
Thu. 09:30 – 10:30 Lecture
Thu. 10:30 – 12:30 LAB
1 sept to 8 dec 2016
IFT 6390 -Fundamentals of machine learning (2nd cycle)Basic elements of statistical and symbolic learning algorithms. Applications in data mining, pattern recognition, nonlinear regression and time-series data.

Note: Knowledge of numerical analysis recommend, such as in IFT 2425.

4Wed. 09:30 – 11:30 Lecture
Thu. 09:30 – 10:30 Lecture
Thu. 10:30 – 12:30 LAB
1 sept to 8 dec 2016
Lacoste-Julien SimonIFT 6269 – Probabilistic graphical models and learningRepresenting systems as graphical probabilistic models, inference in graphical models, learning parameters from data.4Tue. 14:30 – 16:30 Lecture
Fri 13:30 – 15:30 Lecture
2 sept to 6 dec 2016
Charlin Laurent6-602-07 (HEC)- Applied multidimensional analysisData mining, factorial analysis, selecting variables and models, logistic regression, grouping analysis, survival analysis, missing data3Mon. 12:00 – 15:00 Lecture29 aug to 5 dec 2016
Bengio Yoshuan/an/an/an/an/a
Courville Aaronn/an/an/an/an/a
Pal Christophern/an/an/an/an/a

Thu.