ECE 59500 - Deep Learning for Computer Vision

Lecture Hours: 3 Credits: 3

Areas of Specialization(s):

Counts as:
CMPE Special Content Elective
EE Elective

Experimental Course Offered: Fall 2020

ECE 26400 Minimum grade of B, ECE 36900 Minimum grade of B, ECE30200 Minimum grade of B, MA 26200 or MA 26500 Minimum grade of B, ECE 60000 for graduate students.

Requisites by Topic:
Advanced C Programming, Discrete Math, Probability and Linear Algebra

Catalog Description:
An introduction to modern computer vision using methods from machine learning and deep learning. Covers segmentation, object classification and localization, activity classification and localization, semantic segmentation, depth reconstruction, 3D reconstruction, generative adversarial networks, image and video captioning, and image and video retrieval. The course will cover fundamental topics as well as recent advances from the literature.

Required Text(s):
  1. Computer Vision: A Modern Approach, 2nd Edition, David A. Forsyth and Jean Ponce Pearson, ISBN No. 13: 978-0136085928.
Recommended Text(s):
  1. Deep Learning, Ian Goodfellow, Yoshua Bengio, and Aaron Courville, MIT Press, ISBN No. 13: 978- 0262035613.

Lecture Outline:

Lectures Lecture Topics
Topic Fundamentals of neural networks and deep learning
Topic Graph-based image segmentation
Topic Object classification and localization
Topic LSTMs
Topic Activity classification and localization
Topic Semantic segmentation
Topic depth reconstruction
Topic 3D reconstruction
Topic generative adversarial networks
Topic Image and video captioning
Topic Image and video retrieval