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 |