ECSE 551 — Machine Learning for Engineers

Introduction to machine learning: challenges and fundamental concepts. Supervised learning: Regression and Classification. Unsupervised learning. Curse of dimensionality: dimension reduction and feature selection. Error estimation and empirical validation. Emphasis on good methods and practices for deployment of real systems.

• Rating: 4.63 out of 5 from 363 student reviews
• Difficulty: 3.21 out of 5
• Credits: 4
• Faculty: Faculty of Engineering
• Department: Electrical & Computer Engr
• Taught by: Mark Coates
• Prerequisite(s): ((ECSE 250 or COMP 250) and (ECSE 205 or MATH 323)) or GEPR 221

Sections offered

• Section 001 (Lec), Mon Wed 2:35-3:55 pm — 0 seats open
• Section 002 (Lec), Mon Wed 2:35-3:55 pm — 0 seats open
• Section 003 (Lec), Mon Wed 2:35-3:55 pm — 3 seats open
• Section 004 (Tut), Mon 8:35-10:25 am — 3 seats open
• Section 005 (Tut), Tue 3:35-5:25 pm — 0 seats open