Symbolic Machine Learning

The course cannot be taken simultaneously with:

Symbolic Machine Learning (B4M36SMU)

The course is a substitute for:

Symbolic Machine Learning (B4M36SMU)

Garant předmětu:

Lecturer:

Tutor:

Supervisor:

Department of Computer Science

Synopsis:

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.

Requirements:

Syllabus of lectures:

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Study Objective:

Study materials:

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Further information:

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

No time-table has been prepared for this course

The course is a part of the following study plans:

• Open Informatics - Artificial Intelligence (compulsory course of the specialization)
• Open Informatics - Bioinformatics (compulsory course of the specialization)
• Open Informatics - Data Science (compulsory course of the specialization)
• Medical Electronics and Bioinformatics - Specialization Image Processing (compulsory elective course)
• Medical Electronics and Bioinformatics - Specialization Signal Processing (compulsory elective course)
• Medical Electronics and Bioinformatics - Specialization Bioinformatics (compulsory elective course)
• Medical Electronics and Bioinformatics - Specialization Medical Instrumentation (compulsory elective course)