courses

COMP 579 Reinforcement Learning

Offered By: Doina Precup, Semester: Winter 2022

Computer Science (Sci) : Bandit algorithms, finite Markov decision processes, dynamic programming, Monte-Carlo Methods, temporal-difference learning, bootstrapping, planning, approximation methods, on versus off policy learning, policy gradient methods temporal abstraction and inverse reinforcement learning.

  https://www.mcgill.ca/study/2021-2022/courses/comp-579

COMP 551 – Applied Machine Learning

Offered By: Reihaneh Rabbany, Semester: Winter 2022

This course covers a selected set of topics in machine learning and data mining, with an emphasis on good methods and practices for deployment of real systems. The majority of sections are related to commonly used supervised learning techniques, and to a lesser degree unsupervised methods. This includes fundamentals of algorithms on linear and logistic regression, decision trees, support vector machines, clustering, neural networks, as well as key techniques for feature selection and dimensionality reduction, error estimation and empirical validation.

  http://www.reirab.com/comp551.html

COMP 599 – Natural Language Understanding with Deep Learning

Offered By: Siva Reddy, Semester: Winter 2022

The field of natural language processing (NLP) has seen multiple paradigm shifts over decades, from symbolic AI to statistical methods to deep learning. We review this shift through the lens of natural language understanding (NLU), a branch of NLP that deals with “meaning”. We start with what is meaning and what does it mean for a machine to understand language? We explore how to represent the meaning of words, phrases, sentences and discourse. We then dive into many useful NLU applications.

  https://www.mcgill.ca/study/2021-2022/courses/comp-599