Security and Fairness of Deep Learning (18-739, Spring 2020)

Lectures: Tuesdays and Thursdays

Lectures: Tuesdays and Thursdays

CMU @Pittsburgh
1:30pm-2:50pm Eastern; HH 1107
CMU @Silicon Valley
10:30am-11:50pm Pacific; B23 211

Instructors

Professor: Piotr Mardziel
Office Hours: Thursdays
3:00-4:00pm Eastern / 12:00-1:00pm Pacific
@PGH zoom
@SV B23 114
Teaching Assistant: Zifan Wang
Office Hours: Wednesday
3:00-4:00pm Eastern / 12:00-1:00pm Pacific
@PGH CIC 2206
@SV zoom
Teaching Assistant: Caleb Lu
Office Hours: Tuesday
3:00-4:00pm Eastern / 12:00-1:00pm Pacific
@PGH zoom
@SV B23 129B
Teaching Assistant: Zhiqian Qiao
Office Hours: Tuesday
12:00-1:00pm Eastern / 9:00-10:00am Pacific
@PGH NSH 1609
@SV TBD

The Course

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 and critique weekly readings. Prior knowledge of machine learning, deep learning, and security concepts are useful but not required.

Textbook

  • Goodfellow, Ian, Yoshua Bengio, and Aaron Courville. Deep Learning. MIT press, 2016.

Grading

  • 5 Homeworks: 90%
  • Class Participation: 10%

Total of 8 late days (24 hours each) can be used throughout the semester. No more than 3 late days may be used on a single assignment.

Previous Offerings

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