Course detail
Multi-valued Logic Applications
FSI-SAL Acad. year: 2025/2026 Summer semester
Supervisor
Department
Learning outcomes of the course unit
Prerequisites
Planned learning activities and teaching methods
Assesment methods and criteria linked to learning outcomes
Language of instruction
Czech
Aims
The aim of the course is to introduce students to the mathematical background of artificial intelligence methods and also to teach them how to implement these methods with understanding.
The areas that will be covered in the course, which students will study and program:
1. Nearest Neighbor Method, Decision Trees, Support Vector Machine.
2. Building a neural network for training on tabular data.
3. Convolutional neural networks for working with image data.
4. R-CNN for detecting a particular object in images.
5. Autoencoders and decoders.
Specification of controlled education, way of implementation and compensation for absences
The study programmes with the given course
Programme N-MAI-P: Mathematical Engineering, Master's
branch ---: no specialisation, 4 credits, elective
Type of course unit
Lecture
26 hours, compulsory
Syllabus
1. Relationship of artificial intelligence methods to expert systems.
2.-3. Machine learning methods (kNN, decision trees, SVM, etc.).
4.-5. Basic neural network design for tabular data, explanation of back-propagation.
6.- 7. Convolutional neural networks (convolution, pooling, batch normalization).
8. Autoencoders and decoders.
9. Pre-trained CNN – implementation, properties
10. R-CNN (convolutional neural network for image retrieval), transformers.
11.-12. Work on semester projects and tutorials.
13. Presentation of final projects and evaluation.
Computer-assisted exercise
13 hours, compulsory
Syllabus
Lectures are in Matlab or Python using libraries: scikit-learn, pandas, keras, pytorch.
1. Design of expert system in Matlab (connection with fuzzy logic).
2.-3. Implementation of kNN, decision trees, and SVM methods on different data. Test and validation data.
4.-5. Design of neural networks for prediction on given data (e.g., medical data, economic indicators, etc.).
6.-7. Processing of image databases for designing convolutional neural networks (recognition of handwritten digits, geometric shapes, animals).
8. Autoencoders and decoders – implementation for noise reduction, image retrieval, and data dimensionality reduction.
9. Pre-trained CNN – ResNet, GoogleNet
10. R-CNN, YOLO on real data.
11.-12. Semester project consultation.
13. Presentation and evaluation of work.