ECE 59500 - Deep Learning for Computer VisionLecture Hours: 3 Credits: 3
This is an experiential learning course.
CMPE Special Content Elective
Experimental Course Offered: Spring 2021
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
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.
- Computer Vision: A Modern Approach, 2nd Edition, David A. Forsyth and Jean Ponce Pearson, ISBN No. 13: 978-0136085928.
- Deep Learning, Ian Goodfellow, Yoshua Bengio, and Aaron Courville, MIT Press, ISBN No. 13: 978- 0262035613.
Learning Outcomes:A student who successfully fulfills the course requirements will have demonstrated:
- Ability to design and implement an object classifier and localizer. [1,2,4,6,7]
- ability to design and implement an activity classifier and localizer. [1,2,4,6,7]
- ability to design and implement an image or object captioning system. [1,2,4,6,7]
- ability to present research results in NLP to peers. [3,5]
|Topic||Fundamentals of neural networks and deep learning|
|Topic||Graph-based image segmentation|
|Topic||Object classification and localization|
|Topic||Activity classification and localization|
|Topic||generative adversarial networks|
|Topic||Image and video captioning|
|Topic||Image and video retrieval|
Engineering Design Content:
Engineering Design Consideration(s):