Courses and schedules - Winter 2020 (Copy of Winter 2019)
| Professor | Course | Description | Credits | Schedule | Dates |
|---|---|---|---|---|---|
| Simon Lacoste-Julien | 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 |
| Aaron Courville | IFT 6135 – Apprentissage de représentations (Winter 2019) | 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 |
| Ioannis Mitliagkas | IFT-6085 – Theoretical principles for deep learning | Research 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. | 4 | Wednesday 9:30 – 11:30 Thursday 9:00 – 11:00 | 08-01-2020 – 16-04-2020 |
| Guillaume Rabusseau | IFT-6760a – Séminaire en apprentissage automatique (Winter 2019) | 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 |
| Irina Rish | IFT 6760b – Continual Learning: Towards “Broad” AI | Stephen 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. | 4 | Monday 3:00 – 5:30 Thursday 4:30 – 6:30 | 02-02-2020 – 01-06-2020 |
| Emma Frejinger | IFT-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. | 4 | Monday 1:30-3:29 Thursday 11:30-1:29 | 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. | 3 | Friday 6:30 – 9:30 | 11-01-2019 – 12-04-2019 |
| Bernard Gendron | IFT 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. | 4 | Monday 11:30 – 1:29 Tuesday 10:30 – 12:29 | |
| Guillaume Lajoie | MAT6115 Dynamical Systems | Introduction 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. | 3 | Tuesday 9:30 – 12:30 | 07-01-2020 – 28-04-2020 |
| Blake Richards | COMP 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. | 3 | Tuesday 1:00 – 2:30 Thursday 1:00 – 2:30 | 07-01-2020 – 9-04-2020 |
Courses and schedules - Fall 2019
| Noms Professeurs | Cours/sigles | Descriptions | Crédits | Horaires | Dates |
|---|---|---|---|---|---|
| Simon Lacoste-Julien | IFT 6269 – Modèles Graphiques probabilistes et apprentissage | System Representation as probabilistic graphical models, inference in graphical models, learning parameters from data. | 4 | Tuesday 2:30 – 4:30 Friday 1:30 – 3:30 | 03-09-2019 – 06-12-2019 |
| Ioannis Mitliagkas | IFT 6390 – Fondements de l’Apprentissage Machine | Basic elements of statistical and symbolic learning algorithms. Examples of applications in data mining, pattern recognition, nonlinear regression, and time data. | 4 | Section 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 |
| Laurent Charlin | MATH 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). | 3 | Wednesday 8:30 – 11:30 | 04-09-2019 – 04-12-2019 |
| Laurent Charlin | MATH 80629 Apprentissage automatique I : Analyse des Mégadonnées et Prise de décision | 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). | 3 | Thursday 8:30 – 11:30 | 05-09-2019 – 05-12-2019 |
| Jackie C. K. Cheung | COMP 550 – 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 | Tuesday 11:35 – 12:55 Thursday 11:35 – 12:55 | 03-09-2019 – 10-12-2019 |
| William L. Hamilton | COMP 551- Applied Machine Learning | The 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. | 3 | Monday 4:00 – 5:30 Wednesday 4:00 – 5:30 | 06-09-2019 – 06-12-2019 |
| Rabbany Reihaney | COMP 596 – Network Science | Networks 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. | 3 | Tuesday 10:00 – 11:30 Thursday 10:00 – 11:30 | 03-09-2019 – 09-12-2019 |
| Liam Paull | IFT 6757 – Véhicule Autonomes | 3 | Monday 10:30 – 12:30 Wednesday 11:30 – 1:30 | 02-09-2019 – 04-12-2019 | |
| Guy Wolf | MAT 6480W / STT 6705V – Special Topics in Geometric Data Analysis | 3 | Wednesday 3:30 – 5:00 Thursday 3:30 – 5:00 | 04-09-2019 – 04-12-2019 |
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 |