20656 - METHODS AND DATA ANALYTICS FOR RISK ASSESSMENT
Department of Computing Sciences
Course taught in English
Course Director:
ALESSANDRO SANZENI
ALESSANDRO SANZENI
Suggested background knowledge
This course integrates elements of elementary probability, statistics, computer science, and programming. Although prior familiarity with these tools is not required, it can significantly enhance the learning experience. Such familiarity fosters a deeper engagement with the material, enabling students to grasp concepts more effectively.
Mission & Content Summary
MISSION
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.
CONTENT SUMMARY
- Bayesian approach to probability
- Bayesian methods in risk assessment
- Bayesian networks and causal graphs
- Unsupervised learning in the Bayesian framework
- Supervised learning in the Bayesian framework
- Case studies: risk prediction in business, software, hardware, law, medicine, insurance, and climate
- Introduction to Monte Carlo methods
- Monte Carlo methods for risk assessment
Intended Learning Outcomes (ILO)
KNOWLEDGE AND UNDERSTANDING
At the end of the course student will be able to...
- Comprehend the Bayesian (and frequentist) perspectives on data analysis and inference.
- Understand the principles and methods of supervised and unsupervised learning from a Bayesian viewpoint.
- Develop an intuition for constructing a Bayesian data analysis pipeline, encompassing data collection through to final predictions.
- Grasp the concept and applications of Monte Carlo simulations and their role in Bayesian analysis.
APPLYING KNOWLEDGE AND UNDERSTANDING
At the end of the course student will be able to...
• 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
Teaching methods
- Lectures
- Practical Exercises
DETAILS
Assessment methods
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ATTENDING AND NOT ATTENDING STUDENTS
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.
Teaching materials
ATTENDING AND NOT ATTENDING STUDENTS
TBD
Last change 25/02/2025 17:05