Summary of Study | Summary of Branches | All Subject Groups | All Subjects | List of Roles | Explanatory Notes               Instructions

---

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

Web page:

https://cw.fel.cvut.cz/b202/courses/smu/start

Anotation:

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:

Literature:

Requirements:

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)