Overview

Description

CALENDAR DESCRIPTION:

Machine Learning is the study of computer algorithms that improve automatically through experience. Provides students who conduct research in machine learning, or use it in their research, with a grounding in both the theoretical justification for, and practical application of, machine learning algorithms. Covers techniques in supervised and unsupervised learning, the graphical model formalism, and algorithms for combining models. Students who have taken CMPT 882 (Machine Learning) in 2007 or earlier may not take CMPT 726 for further credit.

COURSE DETAILS:

Machine learning is the study of computer algorithms that improve automatically through experience, which play an increasingly important role in artificial intelligence, computer science and beyond. The goal of this course is to introduce students to machine learning, starting from the foundations and gradually building up to modern techniques. Students in the course will learn about the theoretical underpinnings, modern applications and software tools for applying deep learning. This course is intended to be an introductory course for students interested in conducting research in machine learning or applying machine learning, and should prepare students for more advanced courses, such as CMPT 727 and CMPT 728. No previous knowledge of machine learning is assumed, but students are expected to have solid background in calculus, linear algebra, probability and programming using Python.

Topics

• Mathematical foundations: review of linear algebra, multivariate calculus and probability
• (Generalized) linear models: linear regression, ridge regression, logistic regression
• Non-linear models: support vector machines, neural networks, k-nearest neighbours
• Regression, binary classification, multinomial classification
• Optimization: gradient descent, stochastic gradient descent, Lagrangian duality

Grading

Materials

MATERIALS + SUPPLIES:

Reference Books:
Machine Learning: A Probabilistic Perspective, Kevin P. Murphy, MIT Press, 2012, 9780262018029

The Elements of Statistical Learning, Trevor Hastie, Robert Tibshirani, and Jerome Friedman, Springer-Verlag, 2009, 9780387848570

All of Statistics, Larry Wasserman, Springer, 2010, 9781441923226

Pattern Recognition and Machine Learning, Christopher M. Bishop, Springer, 2006, 9780387310732

Machine Learning, Tom Mitchell, McGraw Hill, 1997, 9780070428072

REQUIRED READING NOTES: