Machine Learning in Cyber Security Mario Fritz

Registration for this course is open until Friday, 26.10.2018 23:59.



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Machine Learning in Cyber Security


Course Description

Recent advances in Machine Learning has lead to near (or beyond) human-level performance in many tasks - autonomous driving, voice assistance, playing a variety of games. In terms of privacy and security, this is a double-edged sword. ML techniques can be used to efficiently detect and prevent attacks (e.g., intrusion detection). However, their deployment to many real-world sensitive systems (e.g., self-driving cars, the cloud) also makes them susceptible to numerous attacks, such as introducing imperceptible perturbations in inputs and forcing ML systems behave in unintended ways.

The course explores in-depth both of these sides to Machine Learning and Cyber Security. The content addresses the following areas:

  1. Fundamentals of ML
  2. ML for improving security
  3. Attacks on ML models
  4. ML and Privacy


Class Time and Location

Winter Semester 2018/19
Lectures: Wednesday, 12:00 - 14:00
Location: E91, 0.05

Instructor: Mario Fritz
Teaching Assistants: Tribhuvanesh Orekondy, Hossein Hajipour, Kathrin Grosse


Schedule and Syllabus

Event Type Date Description Course Materials
Lecture October 17 (coming soon)  



  • Programming: Basic programming skills and familiarity with Python. All assignments will be in Python.
  • Linear Algebra, Probability, Statistics and Calculus: Introductory level.


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