Catalog Description

E E 526X. Deep Learning: Theory and Practice. (3-0) Cr. 3. F. Prereqs: MATH 207, E E 322. Review of basic theoretic tools such as linear algebra and probability. Machine learning basics will then be introduced to motivate deep learning networks. Different deep learning network architectures will be studied in detail, including their training and implementations. Applications and research problems will also be surveyed at the end of the class.

Lectures Time and Place


There is no required textbook for the course. Some reference books are listed below:


Office Hours: MW 9-10AM; or by appointment

Teaching Assistants

Pan Zhong (ISU net-id: pzhong)
Office Hours: Thursdays 2-3PM; and by appointment