Courses

Courses and schedules - Fall 2019

Noms ProfesseursCours/siglesDescriptionsCréditsHorairesDates
Lacoste-Julien SimonIFT 6269 – Modèles Graphiques probabilistes et apprentissageSystem Representation as probabilistic graphical models, inference in graphical models, learning parameters from data.4Tuesday 2:30 – 4:30
Friday 1:30 – 3:30
03-09-2019 – 06-12-2019
Mitliagkas IoannisIFT 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 :Wednesday 9:30-11:30  et Je 9:30-10:30

Section A1 Thursday 10:30-12:30

Section A102 Thursday 10:30-12:30

03-09-2019 – 05-12-2019
Charlin LaurentMATH 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).3Wednesday 8:30 – 11:3004-09-2019 – 04-12-2019
Charlin LaurentMATH 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).3Thursday 8:30 – 11:3005-09-2019 – 05-12-2019
Cheung C. K. JackieCOMP 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.3Tuesday 11:35 – 12:55

Thursday 11:35 – 12:55

03-09-2019 – 10-12-2019
 Hamilton L. WilliamCOMP 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.3Monday 4:00 – 5:30

Wednesday 4:00 – 5:30

06-09-2019 – 06-12-2019
Rabbany ReihaneyCOMP 596 – Topics in Computer Science 3In this course, subjects are selected from program verification, formal semantics, formal language theory, simultaneous programming, complexity or algorithms, programming language theory, graphics, and other topics related to computing. This course can be repeated for credit with different subjects.3Tuesday 10:00 – 11:30

Thursday 10:00 – 11:30

03-09-2019 – 09-12-2019
Liam PaullIFT 6757 – Véhicule Autonomes
3Monday 10:30 – 12:30

Wednesday 11:30 – 1:30

02-09-2019 – 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.
4Tuesday 2:30 – 14:29
Thursday 1:30 – 3:29
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

4Monday 9:30 – 11:29
Wednesday 12:30 – 2:29
 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.4Wednesday  9:30 – 11:29

Thursday 9:00 – 11:29

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.
4Tuesday 12:30 – 2:29

Thursday 11:30 – 1:29

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.4Monday 1:30-3:29

Thursday 11:30-1:29

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.3Friday 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.4Monday 11:30 – 1:29

Tuesday 10:30 – 12:29

 

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 – 16:29 Sem
Fri 13:30 – 15:29 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

Mon10:30-12:30

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 – 17:29
Fri 14:30 – 16:29
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 – 11:29
Wed 12:30 – 14:29
 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.3Ma 15:45 – 18:45
Je 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 – 11:29
Thu 12:30 – 13:29
 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
Th 9:30 – 10:30 Lect
Th 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 – 16:29 Sem
Fri 13:30 – 15:29 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.4Lu 10:30 – 12:20 Cours
Me 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
Th 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 – 12:29
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 – 16:29
Th 09:30 – 11:29
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
Thr 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
Thr 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 – 11:29 Lecture
Thu 09:30 – 10:29 Lecture
Thu 10:30 – 12:29 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 – 11:29 Lecture
Thu 09:30 – 10:29 Lecture
Thu 10:30 – 12:29 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 – 16:29 Lecture
Fri 13:30 – 15:29 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