Machine Learning and deep learning are fast-growing areas that are profoundly impacting and changing our economy and society. They have become a standard component of many computational problems including those in big data analytics. This course will cover basic concepts, models, and methods in deep learning and machine learning. It will include supervised and unsupervised machine learning methods for regression, classification, clustering, and time series modeling; methods of fitting models; regularization; techniques for addressing overfitting; deep learning and neural network models; a general viewpoint from graphical models; processes for how to refine/implement/test applications of machine/deep learning algorithms.

Credit Breakdown

Lecture: 3
Lab: 0.25
Tutorial: 0.25

Academic Unit Breakdown

Mathematics 11
Natural Sciences 0
Complementary Studies 0
Engineering Science 20
Engineering Design 11

PREREQUISITE(S): ELEC 278 or CISC 235, ELEC 326, or permission of the instructor