Units:

12

Description:

This course will provide an introduction to deep learning methods with emphasis on understanding and improving their security, privacy, and fairness properties. The course will cover basics of machine learning and introduce popular deep learning methods. It will delve into applications of deep learning methods in security, their susceptibility to adversarial manipulation, and techniques for making deep learning robust to adversarial manipulation. It will cover state-of-the-art methods for explaining black-box deep learning models to enhance their transparency. It will also examine methods for deep learning that are designed to respect individual privacy and fairness.

Students will do homework assignments, critique weekly readings, and complete a course project on a topic of their choice. Prior knowledge of machine learning, deep learning, and security concepts are useful but not required.

Last Modified: 2020-01-14 11:00AM

Semesters offered:

• Spring 2020
• Spring 2019
• Spring 2018
• Fall 2017