Courses

Courses and schedules - Winter 2019

 

 

Professor Course Description Credits Schedule Dates
Lacoste-Julien Simon IFT 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.
4 Tuesday 2:30 – 14:29
Thursday 1:30 – 3:29
08-01-2018 – 12-04-2019
Courville Aaron IFT 6135 – Apprentissage de représentations Advanced topics in learning algorithms: deep architectures, neural networks and unsupervised probabilistic models. 4 Monday 9:30 – 11:29
Wednesday 12:30 – 2:29
 07-01-2019 – 10-04-2019
Mitliagkas Ioannis IFT-6085 – Theoretical principles for deep learning This 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. 4 Wednesday  9:30 – 11:29

Thursday 9:00 – 11:29

09-01-2019 – 11-04-2019
Rabusseau Guillaume IFT-6760a – Séminaire en apprentissage automatique This 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.
4 Tuesday 12:30 – 2:29

Thursday 11:30 – 1:29

08-01-2019 – 12-04-2019
Frejinger Emma IFT-6521 – Programmation dynamique 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. 4 Monday 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. 3 Friday 6:30 – 9:30 11-01-2019 – 12-04-2019
Gendron Bernard IFT 6551 – Programmation en nombres entiers 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. 4 Monday 11:30 – 1:29

Tuesday 10:30 – 12:29

 

Courses and schedules - Fall 2018

Professor Names Course Descriptions Credits Hours Dates
Ioannis Mitliagkas IFT 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. 4 Wed 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 Simon IFT 6269 – Probabilistic graphical models and learning Representing systems as graphical probabilistic models, inference in graphical models, learning parameters from data. 4 Tue 14:30 – 16:29 Sem
Fri 13:30 – 15:29 Sem
2018-09-04 – 2018-12-07
Paull Liam  IFT 6757-Autonomous Vehicles Problems 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 experience 4 Mon 10:30 – 12:30
Wed 11:30 – 1:30
05/09/2018 – 05/12/2018
Guillaume Rabusseau IFT3395 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. 3 Wed 9:30 – 11:30 Lect
Th 9:30 – 10:30 Lect
Th 10:30 – 12:30 Lab
2018-09-05 – 2018-12-06
Cheung Jackie Comp 550 (McGill)- 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 2:35 PM – 3:55 PM TR

ENGMC 13

n/a
Lodi Andrea MTH6404 (Poly)- Integer Programming Course 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. 3 n/a n/a
Charlin Laurent 6-602-07 (HEC)- Applied multidimensional analysis Data mining, factorial analysis, selecting variables and models, logistic regression, clustering, survival analysis, missing data 3 Mon 12:00-15:00 Lect n/a
 Charlin Laurent 80-629-17A (HEC) – Machine Learning for Large-Scale Data Analysis & Decision Making Machine 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). 3 Wed 8:30-11:30 Lect n/a
Alain Tapp Data 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/a Mon 9:30 – 10:30

Tue 12:30-14:30

Mon10:30-12:30

9-10-2018 – 03-12-2018
Guillaume Lajoie Dynamical 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. 3 Tue 10:30-12:00

Thu 10:30-12:00

04/09/2018 – 20/12/2018

Courses and schedules - Winter 2018

Noms Professeurs Cours/sigles Descriptions Crédits Horaires Dates
Lacoste-Julien Simon IFT 6132 – Advanced Structured Prediction and Optimization Advanced 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.
4 Tue 14:30 – 17:29
Fri 14:30 – 16:29
23-01-2018 – 30-04-2018
Courville Aaron IFT 6135 – Learning Representations Advanced subjects in learning algorithms: deep architectures, neural networks and non-supervised probabilistic models.
4 Mon 09:30 – 11:29
Wed 12:30 – 14:29
 08-01-2018 – 30-04-2018
Pal Christopher INF 8225 (Poly) – Probabilistic techniques and learning Probabilistic 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. 3 Ma 15:45 – 18:45
Je 13:45 – 16:45
08-01-2018 – 30-04-2018
Precup Doina COMP 767 (McGill) – Advanced Topics: Reinforcement learning Advanced topics in reinforcement learning. 4 Tue 4:00 – 5:30 PM
Thu 4:00 – 5:30 PM
08-01-2018 – 16-04-2018
Pineau Joelle COMP 551 (McGill) – Applied Machine Learning Clustering, 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. 4 Tue 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 Mitliagkas IFT-6085 – Theoretical principles for deep learning Overview 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. 4 Wed 9:30 – 11:30
Thu 9:00 – 11h00
 08-01-2018 – 30-04-2018
Alain Tapp IFT 6271 – Sécurité informatique Confidentiality 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. 3 Mon 09:30 – 11:29
Thu 12:30 – 13:29
 08-01-2018 – 30-04-2018
Charlin Laurent n/a n/a n/a n/a n/a
Bengio Yoshua n/a n/a n/a n/a n/a
Vincent Pascal n/a n/a n/a n/a n/a

 

Courses and schedules - Fall 2017

Noms Professeurs Cours/sigles Descriptions Crédits Horaires Dates
Courville Aaron IFT 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. 4 Wed 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 Simon IFT 6269 – Probabilistic graphical models and learning Representing systems as graphical probabilistic models, inference in graphical models, learning parameters from data. 4 Tue 14:30 – 16:29 Sem
Fri 13:30 – 15:29 Sem
2017-09-05 – 2017-12-08
Paull Liam IFT 6080 – Duckietown Problems 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. 4 Lu 10:30 – 12:20 Cours
Me 11:30-13:30 Cours
2017-09-06 – 2017-12-06
Pal Christopher INF8702 (Poly) – Advanced Computer Graphics Real-time rendering, polygonal and surface rendering. Textures. Parametric curves and surfaces. Local reflexion models. Illumination models. Volume rendering. Artistic rendering and virtual reality. 3 Tue 9:30-12:30 Lab
Fri 12:45 – 15:45 Lect
Precup Doina COMP 652 (McGill) – Machine Learning An overview of state-of-the-art algorithms used in machine learning, including theoretical properties and practical applications of these algorithms. 4 Mon 14:35 – 15:55 Lect
Wed 14:35 – 15:55 Lect
2017-09-05 – 2017-12-07
Pineau Joelle COMP 551 (McGill) – Applied Machine Learning Clustering, 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. 4 Mon 13:05 – 14:25 Lect
Wed 08:35 – 9:55 Lect
2017-09-05 – 2017-12-07
Charlin Laurent 6-602-07 (HEC)- Applied multidimensional analysis Data mining, factorial analysis, selecting variables and models, logistic regression, grouping analysis, survival analysis, missing data 3 Thu 18:45-21:45 Lect n/a
80-629-17A (HEC) – Machine Learning for Large-Scale Data Analysis & Decision Making Machine 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). 3 Wed 8:30-11:30 Lect n/a
Lodi Andrea MTH6404 (Poly)- Integer Programming Course 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. 3 Tue 12:45 – 15:45 Lect n/a
Cheung Jackie Comp 550 (McGill)- 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 Tue 16:05 – 17:25 Lect
Th 16:05 – 17:25 Lect
2017-09-05 – 2017-12-06

Courses and schedules - Summer 2017

Professor Course/Number Descriptions Credits Schedule Dates
Pal Christopher INF6953H (Poly) – Deep Learning Convolutional 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. 3 Tue 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

Professor Course/Number Descriptions Credits Schedule Dates
Lacoste-Julien Simon IFT 6085 – Advanced Structured Prediction Energy-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. 4 Wed 10:30 – 12:29
Fri 15:00 – 16:59
6 Jan to 12 Apr 2017
Courville Aaron IFT 6266 – Learning algorithms Advanced subjects in learning algorithms: deep architectures, neural networks and non-supervised probabilistic models. 4 Mon 14:30 – 16:29
Th 09:30 – 11:29
5 Jan to 13 Apr 2017
Pal Christopher INF 8225 (Poly) – Probabilistic techniques and learning Probabilistic 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 3 Mon 12h45 – 15h45 Lab
Tue 9h30 – 12h30 Lecture
Precup Doina COMP 652 (McGill) – Machine Learning An overview of state-of-the-art algorithms used in machine learning, including theoretical properties and practical applications of these algorithms. 4 Tue 13:05 – 14:25
Thr 13:05 – 14:25
4 Jan to 11 Apr 2017
COMP 767 (McGill) – Advanced Topics: Reinforcement learning Advanced topics in reinforcement learning. 4 Fri 10:05 – 12:55 4 Jan to 11 Apr 2017
Pineau Joelle COMP 551 (McGill) – Applied Machine Learning Clustering, 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. 4 Tue 13:05 – 14:25
Thr 13:05 – 14:25
4 Jan to 11 Apr 2017
Charlin Laurent n/a n/a n/a n/a n/a
Bengio Yoshua n/a n/a n/a n/a n/a
Vincent Pascal n/a n/a n/a n/a n/a

Courses and schedules - Fall 2016

Professor Course/Number Descriptions Credits Schedule Dates
Vincent Pascal IFT 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.

3 Wed 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.

4 Wed 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 Simon IFT 6269 – Probabilistic graphical models and learning Representing systems as graphical probabilistic models, inference in graphical models, learning parameters from data. 4 Tue 14:30 – 16:29 Lecture
Fri 13:30 – 15:29 Lecture
2 sept to 6 dec 2016
Charlin Laurent 6-602-07 (HEC)- Applied multidimensional analysis Data mining, factorial analysis, selecting variables and models, logistic regression, grouping analysis, survival analysis, missing data 3 Mon 12:00 – 15:00 Lecture 29 aug to 5 dec 2016
Bengio Yoshua n/a n/a n/a n/a n/a
Courville Aaron n/a n/a n/a n/a n/a
Pal Christopher n/a n/a n/a n/a n/a