Courses
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Descriptions
EECS 496: Advanced Topics on Deep Learning

Quarter Offered

Winter : 5-8 M ; Liu

Prerequisites

Basic familiarity with deep learning, including convolutional neural networks, LSTMs, and attention mechanisms, understand essential deep learning framework including tensorFlow and pyTorch.

Description

Study of advanced topics of current interest in the field of deep learning, with an emphasis on understanding the network architecture of the pre-trained deep learning models. Selected topics from the following areas will be covered, with an emphasis on practical applications: computer vision, speech recognition, natural language processing, reinforcement learning, and deep learning tools.

COURSE INSTRUCTOR: Prof. Han Liu

REQUIRED TEXTS: None;

COMPUTER USAGE: The python programming language

GRADING: TBD

COURSE OUTCOMES: When a student completes this course, s/he should be able to:

  • Understand the state-of-the-art of deep learning pre-trained models 
  • Become familiar with recent advances in the field of deep learning