Symbolic Machine Learning

Semestr: Summer

Range: 2+2

Completion:

Credits: 6

Programme type:

Study form: Fulltime

Course language: Czech

Time table at FEE

Summary:

The course will explain methods through which an intelligent agent can learn, that is, improve its behavior from observed data and domain knowledge. We will be concerned with techniques for the construction of non-trivial symbolic models (graph or logic based) of the observed world. We will explain the basic principles of computational learning theory, which allows to understand why and when successful learning from data is possible. Besides the standard learning scenario involving a prior fixed learning data set, we will discuss other forms of learning, mainly on-line learning, learning through queries (active learning) and reinforcement learning. The class is given in English to all students.

Keywords:

Course syllabus:

Seminar syllabus:

Literature:

Course textbook available at https://cw.fel.cvut.cz/wiki/courses/b4m36smu/lectures

Stuart Russell and Peter Norvig: Artificial Intelligence: A Modern Approach, Prentice Hall 2010

Luc De Raedt: Logical and Relational Learning, Springer 2008

Marcus Hutter: Universal artificial intelligence, Springer 2005

Examiners:

• doc. Ing. Jiří Kléma, Ph.D.
• prof. Ing. Filip Železný, Ph.D.

Lecturers:

• doc. Ing. Jiří Kléma, Ph.D.
• prof. Ing. Filip Železný, Ph.D.

Instructors:

• doc. Ing. Jiří Kléma, Ph.D.
• prof. Ing. Filip Železný, Ph.D.
• Petr Ryšavý
• Ing. Jáchym Barvínek
• Ing. Martin Svatoš
• Ing. Ondřej Hubáček