Spring 2025

Machine Learning

Listed in: Computer Science, as COSC-247


Lee Spector (Section 01)


Machine Learning algorithms allow computers to be taught to perform tasks without being explicitly programmed. This course is an introduction to machine learning and data mining. The course will explore supervised, unsupervised, ensemble and reinforcement learning. Topics may include: decision tree learning, rule learning, neural networks, support vector machines, Bayesian learning, clustering, hidden Markov model learning, and/or deep learning. The material of this course has some overlap with that of Computer Science 241, but it is permissible to take both.

Requisite: COSC-211. Spring semester. Professor Spector.

How to handle overenrollment: priority to majors

Students who enroll in this course will likely encounter and be expected to engage in the following intellectual skills, modes of learning, and assessment: Mathematical aptitude is essential.

Course Materials


Other years: Offered in Fall 2022, Spring 2023