Course detail

Artificial Intelligence in Sport

CESA-SUINAcad. year: 2023/2024

The course is focused on the commonly used methods from the artificial intelligence area: artificial neural networks, fuzzy logic and fuzzy inference systems, and cluster analysis. The theoretical (basic principles of the methods) and practical (application of methods for solution of classification, regression, or clustering tasks) aspects are studied. Theory is discussed in direct connection with practical examples. All computational techniques are learned during PC exercise using Matlab. This course prepares candidates for the sole use of the methods in their scientific or routine work.

Language of instruction

Czech

Number of ECTS credits

5

Mode of study

Not applicable.

Guarantor

Ing. Marina Filipenská, Ph.D.

Department

Department of Biomedical Engineering (UBMI)

Entry knowledge

The knowledge on the Bachelor´s degree level is requested, namely on numerical mathematics. Knowledge of Matlab is required during PC excercise.

Rules for evaluation and completion of the course

Delimitation of controlled teaching and its procedures are specified by a regulation issued by the lecturer responsible for the course and updated for every year (see Rozvrhové jednotky).
Generally:
- obligatory computer-lab tutorial (missed labs must be properly excused and can be replaced after agreement with the teacher)
- voluntary lecture.

Aims

The goal of the course is to provide the students with sufficient knowledge from artificial intelligence area and to present them the possible use of modern tools of artificial intelligence in acquisition, processing and analysis of data for sport.
Candidates will get knowledge and skills in area of artificial intelligence applications. He will be competent to apply some widespread methods for real tasks solving, naimly to process and analyse data.
During written examination, it is verified, whether the student is able to:
- discuss basic terms from artificial intelligence area
- describe basic methods in this area
- discuss advantages and disadvantages of particular methods
- select and apply appropriate tools to solve the task
- estimate the quality of obtained result and present it in a proper way
- interpret obtained results

Study aids

Not applicable.

Prerequisites and corequisites

Not applicable.

Basic literature

KOZUMPLÍK, J., PROVAZNÍK, I.: Umělá inteligence v medicíně. Elektronická skripta. ÚBMI FEKT VUT v Brně, Brno, 2007. (CS)

Recommended reading

ŠNOREK, M.: Neuronové sítě a neuropočítače. Skripta ČVUT, Praha, 2002 (CS)

eLearning

eLearning: currently opened course

Classification of course in study plans

  • Programme BPC-STC Bachelor's, 3. year of study, winter semester, compulsory

Type of course unit

 

Lecture

26 hours, optionally

Teacher / Lecturer

Ing. Marina Filipenská, Ph.D.

Computer-assisted exercise

26 hours, compulsory

Teacher / Lecturer

MUDr.Ing. Richard Ředina

eLearning

eLearning: currently opened course