1901 E. South Campus Dr., Salt Lake City, UT 84112
1-801-581-6461
Please be informed that our office will be closed for the holiday break starting Friday, December 20th. We will resume operations on Monday, December 30th. Additionally, we will be closed on January 1st in observance of the New Year holiday. For any urgent matters, please email us at register@continue.utah.edu, and we will return your message as soon as we can. Happy Holidays!
This course is an applied introduction to deep learning, a branch of machine learning, that aims to understand and practice the development and application of modern neural networks. In that vein, the course will provide students with a working knowledge of deep learning fundamentals that presents a start point for using more advanced techniques in their future careers. The course will start by reviewing and implementing the main mathematics, statistics, and machine learning principles that will be required during the course. Then, the students will learn how deep learning algorithms extract layered high-level representations of data in order to optimize feature learning, cluster analysis, and classification machine learning tasks. Hands-on activities will provide students with the experience of implementing deep learning architectures for mainly biomedicine applications.
This noncredit class meets with a regular University of Utah credit course.
Online book and material fees may be added up until the first week of class. Students may opt out during the first two weeks of class. For more information, visit the Campus Store Inclusive Access Program's webpage. If you opt out, you will be responsible for obtaining the course materials yourself for the course.
Questions? Call Academic Programs at 801-585-9963 or use our online form.
Days/times/locations to be arranged by instructor.
Date(s) | Day | Time | Location |
---|---|---|---|
01/06/25 - 04/22/25 | TTh | 5:00 pm -6:30 pm | TBA |
Instructor: ABDULMALEK AL-GAHMI
Questions? Call Academic Programs at 801-585-9963 or use our online form.