Courses

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