20656 - METHODS AND DATA ANALYTICS FOR RISK ASSESSMENT

- Probability. - Statistics. - Computer science. - Programming.

Data analytical methods are important for cyber security in two contrasting ways: On the one hand, they can be used for assessing and predicting risk in a large number of areas, and with the trend towards big data and the introduction of new machine learning paradigms this number is growing even further. In particular, Bayesian methods have become more and more important, since they are especially suited for dealing with uncertainty and also work when only a limited number of data is available. The purpose of this course is consequently two-fold: First, to teach the students the aspects of probabilistic thinking and Bayesian methods for risk prediction based on real-life applications from different fields. In the last part of the course, we will also give an introduction to the method of Monte Carlo, which is widely used in the field in industry for simulations of processes involving risk.

• Bayesian approach to Probability
• Bayesian Methods in Risk Assessment
• Bayesian Networks and Causal Graphs
• Unsupervised learning in the Bayesian context
• Supervised learning in the Bayesian context
• Case studies: Risk prediction in business, software, hardware, law, medicine, insurance, climate
• Introduction to Monte Carlo Methods
• Monte Carlo Methods for Risk Assessment

• The Bayesian (and frequentist) view of the world
• Understand the principles and methods of supervised and unsupervised learning in Bayesian terms
• Gain an intuition for the building of a Bayesian data analysis pipeline from data collection to final predictions
• Understand the concept and purpose of Monte Carlo simulations

• Formulate a problem in terms of Bayesian methods
• Design and apply a Bayesian data analysis pipeline using large or small datasets
• Read current literature on the application of data analytics to risk assessment in different domains
• Implement Monte Carlo simulations and use them as tools for solving real-world problems

• Face-to-face lectures
• Case studies /Incidents (traditional, online)

Case studies: Reading/Presenting of literature around risk assessment using data analysis and machine learning.

The exam consists of a theory part and a case discussion part.

• The theory part features open and closed answer questions.
• The case discussion part consists of a short description of a risk prediction task and ask the students to present a roadmap for solving the problem.
• The exam is not open-book: any material outside of what are provided by the instructors is forbidden.

Are communicated at the beginning of the course.