IFT 6132 : Advanced Structured Prediction and Optimization - Winter 2018

Last year version: Winter 2017
Newer version: Winter 2019


Structured prediction is the problem of learning a prediction mapping between inputs and structured outputs, i.e. outputs that are made of interrelated parts often subject to constraints. Examples include predicting trees, orderings, alignments, etc., and appear in many applications from computer vision, natural language processing and computational biology among others.

This is an advanced machine learning course that will focus on the fundamental principles and related tools for structured prediction. The course will review the state of the art, tie older and newer approaches together, as well as identify open questions. It will consist of a mix of faculty lectures, class discussions and paper presentations by students, as well as a research project.

Prerequisite: I will assume that most of the content of IFT 6269 Probabilistic Graphical Models is known by the students.


Class info

Winter 2018 semester:


Some (very tentative) topics – will be updated along the way

Lecture notes + references.