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Course Details

Spring 2018-2019
ELE 571   Graded A-F, P/D/F, Audit

Deep Learning Networks

Sun-Yuan Kung

Machine learning algorithms allow us to induce rules based on empirical training data. Much of its success lies in an effective representation of objects. Both deep neural networks (DNN) and kernel based machines aim at deriving a much expanded vector space to facilitate larger scale (big) data mining. We shall first cover back-propagation learning algorithms for multi-layer networks, convolution and recurrent neural networks: compare the essential differences between deep and kernel learning paradigms; and explore effective mapping of both DNN and kernel learning models to highly parallel processing architectures.

Sample reading list:
I. Goodfellow, Y. Bengio and A. Courville, Deep Learning

Oral Presentation(s) - 25%
Term Paper(s) - 50%
Problem set(s) - 25%

Other Requirements:
Not Open to First Year Undergraduates.

Other information:
The grade will be based on course projects.


Schedule/Classroom assignment:

Class numberSectionTimeDaysRoomEnrollmentStatus
42586 L01 09:30:00 am - 10:50:00 am M W   Friend Center   111   Enrolled:9 Limit:16