Software Development and Data Technologies

This course provides a comprehensive introduction to pattern recognition methods and their applications in data analysis and artificial intelligence. Students will learn the theoretical foundations and practical implementation of supervised and unsupervised learning techniques for classification, clustering, and feature extraction. Lectures cover key topics such as Bayesian decision theory, decision trees, random forests, linear discriminants, perceptrons, support vector machines, and generative models. The course also explores dimensionality reduction techniques, including PCA, Fisher discriminant analysis, and manifold learning, and introduces deep learning methods for object recognition and segmentation.