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:  Completion:Z,ZK
Lecturers:  Credits:6
Tutors:  Semester:L

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This course consists of three parts. The first part of the course will explain methods through which an intelligent agent can learn by interacting with its environment, also known as reinforcement learning. This will include deep reinforcement learning. The second part focuses on Bayesian networks, specifically methods for inference. The third part will cover fundamental topics from natural language learning, starting from the basics and ending with state-of-the-art architectures such as transformer. Finally, the last part will provide an introduction to several topics from the computational learning theory, including the online and batch learning settings.

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
MEBIO4_2018 Signal Processing PV 2
MEBIO1_2018 Bioinformatics PV 2
MEBIO2_2018 Medical Instrumentation PV 2
MEOI9_2018 Data Science PO 2
MEOI8_2018 Bioinformatics PO 2

Page updated 27.9.2023 05:50:50, semester: Z/2024-5, Z/2023-4, Send comments about the content to the Administrators of the Academic Programs Proposal and Realization: I. Halaška (K336), J. Novák (K336)