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Machine Learning in Cyber Security
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:
- Fundamentals of ML
- ML for improving security
- Attacks on ML models
- ML and Privacy
Class Time and Location
Winter Semester 2018/19
Lectures: Wednesday, 12:00 - 14:00
Location: E91, 0.05
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.