Course detail
Machine Learning
FSI-VSC Acad. year: 2026/2027 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 familiarize students with machine learning methods and their applications in classification, regression, and clustering. Students will learn about both parametric and non-parametric classification and regression models, as well as key concepts such as error metrics, regularization, cross-validation, gradient descent, and modern approaches, including boosting and Gaussian mixture models. The course bridges theory and practice, focusing on the design and implementation of machine learning models.
Specification of controlled education, way of implementation and compensation for absences
The study programmes with the given course
Programme N-AIŘ-P: Applied Computer Science and Control, Master's
branch ---: no specialisation, 5 credits, compulsory
Type of course unit
Lecture
26 hours, optionally
Syllabus
- Introduction to Machine Learning: the machine learning model life cycle, learning paradigms and task types, model generalization, cross-validation.
- Data Preprocessing: missing values, outliers, normalization/standardization, categorical encoding, variable transformations, basic feature selection.
- Regression: linear and polynomial regression, the least squares method, gradient descent, loss functions, performance metrics, redundant variables (multicollinearity), regularization.
- Linear Regression-Based Classification, Logistic and Regularized Logistic Regression: basic loss functions and performance metrics for classifiers, the impact of class imbalance on training, classifier performance and evaluation.
- Support Vector Machines for Classification and Regression, Kernel Functions.
- Perceptron, Multilayer Perceptron, Extreme Learning Machine, Forward and Backpropagation, Gradient Descent Variants, Methods for Reducing Overfitting.
- Tree-Based Methods for Classification and Regression: decision trees, splitting criteria, pruning, ensemble methods.
- Memory-Based Methods for Classification and Regression: k-nearest neighbors, distance metrics, choice of k and weighting.
- Introduction to Probability Theory, Bayesian Classifier, Gaussian Discriminant Analysis, Naive Bayes Classifier.
- Gaussian Mixture Models, EM Algorithm.
- Clustering: k-means clustering, Gaussian mixture–based clustering, density-based clustering.
- Dimensionality Reduction, Boosting.
- Review.
Computer-assisted exercise
26 hours, compulsory
Syllabus
- Introduction to the programming environment.
- Data preprocessing.
- Linear and polynomial regression using least squares and regularized least squares.
- Classification of linearly separable data.
- Classification of nonlinearly separable data.
- Using multilayer perceptrons for classification and regression tasks.
- Using decision trees for classification and regression tasks.
- Using the k-nearest neighbors method for classification and regression tasks.
- Bayesian classifier, Gaussian discriminant analysis, and Naive Bayes classifier.
- Using Gaussian mixture models for classification and regression tasks.
- Data clustering.
- Dimensionality reduction, Boosting.
- Final assessment.