COMP 551 — Applied Machine Learning

Selected topics in machine learning and data mining, including clustering, neural networks, support vector machines, decision trees. Methods include feature selection and dimensionality reduction, error estimation and empirical validation, algorithm design and parallelization, and handling of large data sets. Emphasis on good methods and practices for deployment of real systems.

• Rating: 3.28 out of 5 from 952 student reviews
• Difficulty: 3.66 out of 5
• Credits: 4
• Faculty: Faculty of Science
• Department: Computer Science
• Taught by: Pascale Gourdeau
• Prerequisite(s): MATH 323 or ECSE 205, COMP 202, MATH 133, MATH 222 (or their equivalents).

Sections offered

• Section 001 (Lec), Mon Wed 8:35-9:55 am — 0 seats open
• Section 002 (Lec), Mon Wed 8:35-9:55 am — 0 seats open