Id: 008106 Credits Min: 3 Credits Max: 3

Description

This introductory course gives an overview of machine learning techniques used in data mining and pattern recognition applications. Topics include: foundations of machine learning, including statistical and structural methods; feature discovery and selection; parametric and non-parametric classification; supervised and unsupervised learning; use of contextual evidence; clustering, recognition with strings; small sample-size problems and applications to large datasets.

Prerequisites

Pre-Reqs: COMP 1020 Computing II, MATH 3220 Discrete Structures ll and MATH 3860 Probability & Statistics I.

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