Course detail

Machine Learning

FEKT-MPC-MLRAcad. year: 2020/2021

Students will gain insight into advanced machine learning methods. They will be able to describe and compare the properties of individual approaches to data classification. They will be able to select and apply a specific approach to a given problem. They will also gain practical experience with current implementations of machine learning methods.

Language of instruction

Czech

Number of ECTS credits

5

Mode of study

Not applicable.

Guarantor

doc. Ing. Radim Kolář, Ph.D.

Department

Department of Biomedical Engineering (UBMI)

Learning outcomes of the course unit

Not applicable.

Prerequisites

Not applicable.

Co-requisites

Not applicable.

Planned learning activities and teaching methods

Not applicable.

Assesment methods and criteria linked to learning outcomes

Not applicable.

Course curriculum

Not applicable.

Work placements

Not applicable.

Aims

Not applicable.

Specification of controlled education, way of implementation and compensation for absences

Not applicable.

Recommended optional programme components

Not applicable.

Prerequisites and corequisites

Not applicable.

Basic literature

Holčík, J. Analýza a klasifikace dat, Akademické nakladatelství CERM, 2012 (CS)
Komprdová, K. Rozhodovací stromy a lesy, Akademické nakladatelství CERM, 2012 (CS)
Buduma N. Fundamentals of Deep Learning, O'Reilly Media, 2017 (CS)

Recommended reading

Not applicable.

eLearning

eLearning: currently opened course

Classification of course in study plans

  • Programme MPC-BIO Master's, 2. year of study, winter semester, compulsory
  • Programme MPC-BTB Master's, 2. year of study, winter semester, compulsory

Type of course unit

 

Lecture

26 hours, optionally

Teacher / Lecturer

Ing. Jiří Chmelík, Ph.D.
Ing. Roman Jakubíček, Ph.D.
doc. Ing. Radim Kolář, Ph.D.

Exercise in computer lab

26 hours, compulsory

Teacher / Lecturer

Ing. Jakub Nemček
Ing. Tomáš Vičar, Ph.D.

eLearning

eLearning: currently opened course