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 from observed data and background knowledge. The learning scenarios will include on-line learning and learning from i.i.d. data (along with the PAC theory of learnability), as well as the active and reinforcement learning scenarios. Symbolic knowledge representations (mainly through logic and graphs) will be used where possible. The course is given in English to all students.

Course outlines:

1. General framework, passive reinforcement learning
2. TD agent, active R/L, Q-learning
3. SARSA agent, state representation, policy search, AIξ agent
4. Universal sequence prediction, AIXI agent; Non-sequential concept learning.
5. Online learning, mistake-bound model
6. Batch learning, PAC-learning model
7. Learning first-order logic conjunctions
8. Learning first-order logic clauses
9. Learning with queries
10. Bayesian networks
11. Bayesian networks
12. Probabilistic (logic) programming
13. Probabilistic (logic) programming

Exercises outline:


Lecture slides available at 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



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
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 16.7.2020 12:51:48, semester: Z,L/2020-1, L/2019-20, Send comments about the content to the Administrators of the Academic Programs Proposal and Realization: I. Halaška (K336), J. Novák (K336)