263-5210-00L  Probabilistic Artificial Intelligence

Courses

Catalogue data

Abstract	This course introduces core modeling techniques and algorithms from machine learning, optimization and control for reasoning and decision making under uncertainty, and study applications in areas such as robotics.
Learning objective	How can we build systems that perform well in uncertain environments? How can we develop systems that exhibit "intelligent" behavior, without prescribing explicit rules? How can we build systems that learn from experience in order to improve their performance? We will study core modeling techniques and algorithms from statistics, optimization, planning, and control and study applications in areas such as robotics. The course is designed for graduate students.
Content	Topics covered: - Probability - Probabilistic inference (variational inference, MCMC) - Bayesian learning (Gaussian processes, Bayesian deep learning) - Probabilistic planning (MDPs, POMPDPs) - Multi-armed bandits and Bayesian optimization - Reinforcement learning
Prerequisites / Notice	Solid basic knowledge in statistics, algorithms and programming. The material covered in the course "Introduction to Machine Learning" is considered as a prerequisite.
Competencies	Subject-specific Competencies Concepts and Theories assessed Techniques and Technologies assessed Method-specific Competencies Analytical Competencies assessed Decision-making assessed Media and Digital Technologies assessed Problem-solving assessed Project Management assessed Social Competencies Communication fostered Cooperation and Teamwork fostered Personal Competencies Creative Thinking assessed Critical Thinking assessed Integrity and Work Ethics fostered

Performance assessment

Performance assessment information (valid until the course unit is held again)
Performance assessment as a semester course
ECTS credits	8 credits
Examiners	A. Krause
Type	session examination
Language of examination	English
Repetition	The performance assessment is only offered in the session after the course unit. Repetition only possible after re-enrolling.
Mode of examination	written 120 minutes
Additional information on mode of examination	100% of the final grade is determined by the session examination. As a compulsory continuous performance assessment task, the project component of the course must be passed on it's own to take the exam. The practical projects are an integral part (60 hours of work, 2 credits) of the course. The project component is assessed on a pass/fail basis. Participation is mandatory. Failing the project results in a failing grade for the overall examination of Probabilistic Artificial Intelligence (263-5210-00L). Students who do not pass the project component are required to de-register from the exam and will otherwise be treated as a no show. Due to the number of registered students, the exam may be paper-based and will most likely take place on a Saturday. The mode of the exam (computer-based or paper-based) will be finalized in end of October, and the exam date will be announced in December.
Written aids	Two A4-pages (i.e. one A4-sheet of paper), either handwritten or 11 point minimum font size. Simple non-programmable calculator.
Digital exam	The exam takes place on devices provided by ETH Zurich.
This information can be updated until the beginning of the semester; information on the examination timetable is binding.

Learning materials

Groups

Restrictions

Offered in

Programme	Section	Type
CAS in Computer Science	Focus Courses and Electives	W
Computational Biology and Bioinformatics Master	Theory	W
Cyber Security Master	Core Courses	W
DAS in Data Science	Machine Learning and Artificial Intelligence	W
Data Science Master	Data Analysis	W
Data Science Master	Core Electives	W
Doctorate Biosystems Science and Engineering	Subject Specialisation	W
Electrical Engineering and Information Technology Master	Specialisation Courses	W
Electrical Engineering and Information Technology Master	Recommended Subjects	W
Computer Science Master	Core Courses	W
Computer Science Master	Minor in Machine Learning	W
Mechanical Engineering Master	Core Courses	W
Mathematics Master	Selection: Further Realms and Some UZH Courses	W
Mathematics Master	Machine Learning	W
Computational Science and Engineering Bachelor	Robotics (continued)	W
Computational Science and Engineering Master	Core Courses (continued)	W
Computational Science and Engineering Master	Robotics (continued)	W
Robotics, Systems and Control Master	Core Courses	W
Space Systems Master	Deep Track Robotics	W+
Statistics Master	Subject Specific Electives	W