Syllabus
Course Description
Introduction to modeling and algorithmic techniques for machines to learn concepts from data. Generalization: underfitting, overfitting, cross-validation.
Tasks: classification, regression, clustering. Optimization-based learning: loss minimization. regularization. Statistical learning: maximum likelihood, Bayesian learning.
Algorithms: nearest neighbour, (generalized) linear regression, mixtures of Gaussians, Gaussian processes, kernel methods, support vector machines, deep learning, sequence learning, ensemble techniques. Large scale learning: distributed learning and stream learning.
Applications: Natural language processing, computer vision, data mining, human computer interaction, information retrieval.
[Note: Lab is not scheduled and students are expected to find time in open hours to complete their work. Offered: F,W,S] Prereq: CS 341 and (STAT 206 or 231 or 241); Computer Science and BMath (Data Science) students only.