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ENGS 108 - Applied Machine Learning

Description

This course will introduce students to modern machine learning techniques as they apply to engineering and applied scientific and technical problems. Techniques such as recurrent neural networks, deep learning, reinforcement learning and online learning will be specifically covered. Theoretical underpinnings such as VC-Dimension, PAC Learning and universal approximation will be covered together with applications to audio classification, image and video analysis, control, signal processing, computer security and complex systems modeling. Students will gain experience with state-of-the-art software systems for machine learning through both assignments and projects. Because of the large overlap in material covered, no student will receive credit for both ENGS 108 and COSC 74/274.

Prerequisites

ENGS 20 or equivalent, MATH 22 or equivalent, ENGS 27 or ENGS 93 or equivalent.

Cross Listed Courses

QBS 108

Offered

Term
Time
Location / Method
Instructor(s)
Term: Fall 2023
Time: E
Location:
Instructors:

George Cybenko


Term: Fall 2022
Time: E
Location:
Instructors:

George Cybenko


Term: Fall 2021
Time: 12
Location:

MacLean B01

Instructors:

George Cybenko