Spring classes begin Jan. 25 as fully remote, 25% transition to in-person Feb. 1. For more information, visit COVID website.
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.
Pre-Reqs: COMP 1020 Computing II, MATH 3220 Discrete Structures ll and MATH 3860 Probability & Statistics I.