Course detail

Artificial Intelligence in Medicine

FEKT-BPC-UIMAcad. year: 2025/2026

The course is oriented on commonly used methods in the field of artificial intelligence: artificial neural networks, cluster analysis, linear classificators, features selection, classificator evaluation. Both theoretical (basic principles of each method) and practical (applications to the problem of classification, regression and clustering) aspects are discussed. The theory is discussed in direct connection with practical examples. All computational techniques are practiced using the Python environment. The course prepares students to independently use the given methods for data analysis in their own 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. The laboratory work is expected knowledge of Python.

 

Rules for evaluation and completion of the course

The conditions for successful completion of the course are specified in the annually updated decree of the course guarantor.

1) Team project (max. 25 points):
- Preparation of an original team project solution and its defence at the end of the semester (according to the guidelines).
- the completion of the assignment and the quality of the presentation of the results by all team members will be evaluated
- plagiarism will result in 0 credit

2) Final exam (max. 75 points):
- Combined form (written and oral)
- three parts in total, each for a maximum of 25 points

Conditions for credit and admission to the final examination:
- obtaining a non-zero number of points for the team project
- a maximum of two excused absences

Conditions for successful completion of the course:
- obtaining credit
- obtaining at least 36 points in the exam
- obtaining a total (i.e. team project and exam) of at least 50 points

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 biomedical data.
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)

Classification of course in study plans

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

Type of course unit

 

Lecture

26 hod., optionally

Teacher / Lecturer

Ing. Marina Filipenská, Ph.D.

Syllabus

1. Introduction to artificial intelligence. Areas of application: classification (into two or more classes), regression, and clustering. Overview of machine learning algorithms.
2. Preparation of measured data: feature description, normalization/standardization, training/testing/validation datasets.
3. Feature selection. Feature extraction (Principal Component Analysis, PCA).
4. Clustering analysis. Hierarchical clustering methods.
5. Non-hierarchical clustering methods: k-means algorithm. Interpretation and validation of clustering output: silhouette analysis.
6. Artificial neural networks. Neuron as a classifier (perceptron), characteristics of the perceptron.
7. Perceptron learning: delta rule. Limitations of the perceptron: linear vs. non-linear problems.
8. Multilayer feedforward network. Backpropagation algorithm. Network parameter optimization.
9. Evaluation of classification and regression outputs. Cross-validation of machine learning models.
10. Linear classifiers: SVM, logistic regression.
11. Probabilistic models. Methods of "Maximum likelihood" and "Maximum a-posteriori probability."
12. Bayesian approach to classification. Naive Bayes classifier.
13. Examples of machine learning methods applied to real-world problems.

Exercise in computer lab

26 hod., compulsory

Teacher / Lecturer

MUDr.Ing. Richard Ředina

Syllabus

1. Basics of vectorization and matrix operations
2. Hierarchical data clustering
3. Non-hierarchical data clustering
4. Feature reduction and principal component analysis
5. Perceptron design (without learning)
6. Neural network design (without learning)
7. Delta rule
8. Forward network learning I
9. Forward network learning I
10. Model validation and evaluation of classification results
11. Linear classification: SVM, logistic regression.