Subject description - BE4M36SMU

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BE4M36SMU Symbolic Machine Learning
Roles:PO, PV Extent of teaching:2P+2C
Department:13136 Language of teaching:EN
Guarantors:Železný F. Completion:Z,ZK
Lecturers:Kuželka O., Železný F. Credits:6
Tutors:Too many persons Semester:L


The course will explain methods through which an intelligent agent can learn, that is, improve its behavior by interacting with the environment. The learning scenarios will include Concept learning: we will study online learning and batch learning from i.i.d. data. We will define the mistake-bound and PAC model of learning. Strong emphasis will be on logical representations of learned knowledge, including operators for generalization of logic clauses. Learning probability distributions with a graphical model (Bayes Networks) Reinforcement learning Universal learning with the Kolmogorov prior. Time permitting, we will also discuss active learning with queries. The lectures are given in English for all students.

Course outlines:

Exercises outline:




Subject is included into these academic programs:

Program Branch Role Recommended semester
MEOI7_2018 Artificial Intelligence PO 2
MEBIO3_2018 Image Processing PV 2
MEOI9_2016 Data Science PO 2
MEBIO2_2018 Medical Instrumentation PV 2
MEBIO1_2018 Bioinformatics PV 2
MEOI7_2016 Artificial Intelligence PO 2
MEOI8_2016 Bioinformatics PO 2
MEBIO_2018 Common courses PV 2
MEOI9_2018 Data Science PO 2
MEOI8_2018 Bioinformatics PO 2
MEBIO4_2018 Signal Processing PV 2

Page updated 22.6.2021 19:54:31, semester: L/2021-2, L/2020-1, Z,L/2022-3, Z/2021-2, Send comments about the content to the Administrators of the Academic Programs Proposal and Realization: I. Halaška (K336), J. Novák (K336)