Artificial Intelligence in Medicine
FEKT-BPC-UIMAcad. year: 2020/2021
The course focuses on the basic types of neural networks (with backpropagation, Hamming and Kohonen network). The second part focuses on the hierarchical and non-hierarchical cluster analysis. The third part is focused on the theory of fuzzy sets, fuzzy relations, fuzzy logic, fuzzy inference and approximate reasoning procedures. The following are the methods for relevant features selection and for evaluation of the results obtained by above tools of artificial intelligence.
Learning outcomes of the course unit
The graduate of the course is capable of:
- understanding principles of neural networks (with backpropagation errors, Hamming, Kohonen network),
- implementing the cluster analysis using the non-hierarchical or hierarchical methods,
- explaining the principle of fuzzy inference and approximate reasoning,
- performing selection of the most relevant features for further analysis,
- evaluating the performance of machine learning algorithms,
- giving examples of biomedical areas where above methods are widely used.
The knowledge on the Bachelor´s degree level is requested, namely on numerical mathematics. The laboratory work is expected knowledge of Matlab programming environment.
Recommended optional programme components
Recommended or required reading
Kozumplík, J., Provazník, I.: Umělá inteligence v medicíně. Elektronická skripta. ÚBMI FEKT VUT v Brně, Brno, 2007. (CS)
Šnorek, M.: Neuronové sítě a neuropočítače. Skripta ČVUT, Praha, 2002 (CS)
Giarratano, J.C., Riley, G.D.: Expert Systems. Principles and Programming (4th Edition). Course Technology, Thomson Learning Inc., Canada, 2005, ISBN 0-534-38447-1 (EN)
Jura, P.: Základy fuzzy logiky pro řízení a modelování.VUT v Brně, nakl. VUTIUM, Brno 2003, ISBN 80-214-2261-0 (CS)
Romesburg,H.,Ch.: Cluster Analysis for Researchers. Lulu Press, North Carolina, 2004 (EN)
Gan G., Ma Ch., Wu J.: Data Clustering. Theory, Algorithms and Applications. ASA-SIAM Series on Statistics and Applied Probability, Philadelphia, 2007 (EN)
Planned learning activities and teaching methods
Teaching methods include lectures and computer laboratories. Course is taking advantage of e-learning system. Students have to solve two tests (in the middle and at the end of semester, during the laboratories).
Assesment methods and criteria linked to learning outcomes
Language of instruction
1. Introduction to Artificial Intelligence.
2. Artificial neural networks, the neuron and its characteristics, the neuron as a classifier.
3. Learning the neuron with binary and real inputs and outputs, single-layer perceptron.
4. Multi-layer feed-forward network, the backpropagation algorithm.
5. Hamming network, Kohonen network.
6. Cluster analysis, hierarchical cluster analysis.
7. Non-hierarchical cluster analysis, k-means algorithm.
8. Fuzzy sets, fuzzy relations.
9. Logic, fuzzy logic, fuzzy inference, approximate reasoning.
10. Features selection and decorrelation.
11. Evaluation of classification, prediction and approximation algorithms.
12. Biomedical applications of machine learning tools.
Gaining knowledge about artificial neural networks, introduction to Hierarchical and non-hierarchical cluster analysis. Introduction to the theory of fuzzy sets, fuzzy relations, fuzzy logic, fuzzy inference methods as approximate reasoning. Furthermore, methods for relevant features selection and for evaluation of the results obtained using above approaches.
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).
- obligatory computer-lab tutorial (missed labs must be properly excused and can be replaced after agreement with the teacher)
- voluntary lecture.
Classification of course in study plans
- Programme BPC-BTB Bachelor's, 3. year of study, winter semester, 5 credits, compulsory