Course detail

Artificial Intelligence in Sport

CESA-SUINAcad. year: 2022/2023

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.

Learning outcomes of the course unit

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

Prerequisites

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

Co-requisites

Not applicable.

Planned learning activities and teaching methods

Teaching methods include lectures and computer exercises. Course is taking advantage of e-learning system. Students have to write two tests during the course.

Assesment methods and criteria linked to learning outcomes

Not applicable.

Course curriculum

1. Introduction to Artificial Intelligence. Possible applications: classification (to two or more classes), regression and clustering. Short review of machine learning algorithms
2. Measured data preparation: features, normalization, informative features selection, decorrelation
3. Evaluation of quality of the results of classification, regression or clustering
4. Artificial neural networks, the neuron and its characteristics. The neuron as a classifier. Linear vs. non-linear tasks
5. Learning the neuron with binary and real inputs and outputs, single-layer perceptron
6. Multi-layer feed-forward network, the backpropagation algorithm
7. Hamming network, Hopfield network, Kohonen network
8. Examples of artificial neural network application in real tasks solution
9. Cluster analysis, hierarchical cluster analysis
10. Non-hierarchical cluster analysis, k-means algorithm
11. Examples of clustering application in real tasks solution
12. Fuzzy sets, fuzzy relations, fuzzy logic. Fuzzy clustering
13. Approximate reasoning. Fuzzy inference systems
14. Examples of fuzzy inference systems application in real tasks solution

Work placements

Not applicable.

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.

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

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.

Recommended optional programme components

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

Classification of course in study plans

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

Type of course unit

 

Lecture

26 hod., optionally

Teacher / Lecturer

Computer-assisted exercise

26 hod., compulsory

Teacher / Lecturer

Elearning