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. Fall 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.
Section 01
Tu 2:30 PM - 3:50 PM SCCE A131
Th 2:30 PM - 3:50 PM SCCE A131
| ISBN | Title | Publisher | Author(s) | Comment | Book Store | Price |
|---|---|---|---|---|---|---|
| Machine Learning with PyTorch and Scikit-Learn | Packt Publishing Ltd., 2022 | Sebastian Rashka, Yuxi (Hayden) Liu, and Vahid Mirjalili | TBD |