Pattern recognition

Within this course, students delve into advanced pattern recognition methods, including speech and other audio signals, computer images, and multimodal data. They are introduced to the mathematical foundations essential for understanding and implementing pattern recognition algorithms. The focus is on deep learning and neural network architectures, where students familiarize themselves with advanced models such as convolutional and recurrent neural networks, and explore new trends in deep machine learning methods. The course also covers practical applications of pattern recognition methods in industry and research, with case studies from various fields. Ethical and responsible use of pattern recognition technologies is addressed, emphasizing issues of privacy, bias, and decision automation. Special attention is also given to testing pattern recognizers and appropriately evaluating the probability of recognition errors.

To acquaint students with the advanced mathematical and computational approaches to pattern recognition by classification and analysis.

• lectures,
• individual consultations,
• seminar projects.