263-5210-00: Probabilistic Artificial Intelligence - ETH Zürich

Computer Science

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.

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.

263-5210-00 Section 01 - Fundamentals of Inference (open in new window)

263-5210-00 Section 02 - Bayesian Linear Regression (open in new window)

263-5210-00 Section 03 - Gaussian Processes (open in new window)