DATA440L - Machine Learning

4 Credit(s)

Liberal Arts
This course provides a broad introduction to automated learning from data. Machine learning is the name given to the collection of techniques that allow computational systems to adaptively improve their performance by learning from past observed data. The course introduces the theoretical underpinnings of learning from data, the study of learning algorithms, as well as machine learning applications. Topics include: supervised learning (linear models, SVMs, MLPs) and unsupervised learning (K-means, GMMs), learning theory (generalization theory, bias/variance tradeoffs; Vapnik-Chervonenkis dimension); regularization methods, validation and models selection.

Prerequisite(s): MATH 330L, MATH 210L, CMPT 436N