Course Information

Time and Location:

Instructor: Chandan Reddy

Course Description:

Deep Learning has gained a lot of popularity due to its recent breakthrough results in many real-world applications such as speech recognition, machine translation, image understanding, and robotics. This course introduces the advanced topics and research in the field of deep learning. This course is mainly designed for students who are interested in doing deep learning research. Students are expected to have a fundamental knowledge of deep learning.

Prerequisites:

• CS5984 Deep Learning (or) CS5824 Advanced Machine Learning.

Books:

The material for this course will be adapted from a wide range of sources, and most of the class discussions will be based on recently published papers in deep learning. While there is no single textbook that will be used in this course, the students might find the following books to be useful.

• Ian Goodfellow, Yoshua Bengio, and Aaron Courville. Deep learning. MIT Press, 2016.
• Simon O. Haykin, Neural Networks and Learning Machines, Pearson Education, 3rd Edition, 2008.
• Christopher M. Bishop, Neural networks for pattern recognition, Oxford university press, 1995.

Class Presentations:

Paper presentations will be a major focus of this course. The discussions will cover some of the recent research papers from different areas of deep learning. Students are encouraged to talk and discuss research ideas with other students to explore new research ideas.

Research Project:

One of the major components of this course is the final project. In this project, students will investigate some interesting aspect of deep learning and apply it to a real-world problem. The main purpose of this project is to enable the students to do research in the field of deep learning, and be able to present their work as a research paper. More details about the project proposal and project submission will be provided at a later time.

Grading Policies:

Accommodations Statement:

Honor Code Statement: