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CESA-SUINAcad. 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
Number of ECTS credits
Mode of study
Guarantor
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Entry knowledge
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
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Basic literature
Recommended reading
Elearning
Classification of course in study plans
Lecture
Teacher / Lecturer
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.
Computer-assisted exercise
1. Basics of vectorization and matrix operations2. Hierarchical data clustering3. Non-hierarchical data clustering4. Feature reduction and principal component analysis5. Perceptron design (without learning)6. Neural network design (without learning)7. Delta rule8. Forward network learning I9. Forward network learning I10. Model validation and evaluation of classification results11. Linear classification: SVM, logistic regression.