AIDA.2205 Machine Learning

Id: 042962 Credits: 3-3

Description

This course provides a rigorous, theory-focused introduction to machine learning grounded in linear algebra, probability, and statistical inference. Students study core learning techniques, including linear and logistic regression, Bayesian inference, Gaussian models, support vector machines, clustering methods (k-means and Gaussian mixture models), and neural networks, and conclude the course with a team project. Emphasis is placed on mathematical formulation, objective functions, optimization, and generalization behavior. The course complements an applied Machine Learning Studio by focusing on theoretical foundations, analytical reasoning, and model assumptions rather than software implementation.

Prerequisites

AIDA.1020 Data Structures in Python II, or COMP.1020 Computing II, and MATh.2210Introduction to Linear Algebra, and MATH.3850 Applied Statistics.

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Course prerequisites/corequisites are determined by the faculty and approved by the curriculum committees. Students are required to fulfill these requirements prior to enrollment. For courses offered through online or GPS delivery, students are responsible for confirming with the instructor or department that all enrollment requirements have been satisfied before registering.