Graduate Course Descriptions

The following directory lists the graduate courses which the University expects to offer, although the University in no way guarantees that all such courses will be offered in any given academic year, and reserves the right to alter the list if conditions warrant. Click on the links below for a list of courses in that subject area. You may then click “View Classes” to see scheduled classes for individual courses.

5837. Embedded Machine Learning

3.00 credits

Prerequisites: Open to graduate students in the CSE program, others with consent. Recommended preparation: CSE 3500; MATH 2210Q; STAT 3025, 3345, 3375, or MATH 3160.

Grading Basis: Graded

This course will focus on the recent advances in efficient processing of machine learning. Topics include (1) basic machine learning models (inference and training), including deep convolutional neural networks (DCNN), recurrent neural networks (LSTM, GRU, etc.), Transformer (BERT, RoBERTa, DistilBERT, etc.); (2) different applications including object recognition/detection, super resolution, neural machine translation, etc.; (3) effective machine learning accelerations including model compression, quantization, neural architecture search (NAS), GPU and FPGA implementations, dedicated hardware such as Google TPU or IBM TrueNorth; (4) emerging topics, such as federated learning for edge computing systems.

No classes found.