We would like to announce the lecturers and contents of the elective courses Computer Science Skills 2 and Topical Research Themes 1 and 2 for the academic year 2025/2026.

All elective courses for each study programme are published in the Course Syllabuses which can be found at the bottom of the programme descriptions on the Study subpage. The dates of enrolment are published on the Enrolment page.

Opomba: * predmet se izvaja v angleškem jeziku.

Undergraduate study, 1st Cycle

Computer Science Skills 2

Computer Vision in Biometrics

(Course is taught in English.)

Prerequisites: Good programming skills and knowledge of Python.

Course content is divided into three main parts:
(I) data acquisition and the importance of unbiased and balanced datasets,
(II) image detection and segmentation for further object analysis,
(III) recognition of people and objects.
Students will be assessed through 5 seminar assignments.

Weekly content:

• Week 1: Introduction to computer vision and biometrics. Students learn to import, display, save, and modify images—resizing, cropping, color conversion, brightness, and contrast. They practice image enhancement using histogram equalization, convolution for sharpening and blurring, edge detection, and image segmentation.
• Week 2: Overview of biometric modalities: face and facial parts (ears, eyes and parts of eyes), fingers, gait, speech, action recognition, etc.
• Week 3: Introduction to object detection. Students explore several preprocessing techniques for object detection. They learn how Haar cascade pyramids work and implement face detection using webcam capture.
• Week 4: Deep learning models are introduced. Students learn about key object detection models such as Fast R-CNN and YOLO, and preprocessing techniques for deep learning. Evaluation of detection algorithms and implementation approaches are discussed.
• Week 5: Students learn semantic image segmentation with biometric examples, from basic thresholding to advanced segmentation with deep models.
• Week 6: Continuation with U-Net and Mask R-CNN models, with evaluation on student datasets.
• Week 7: Introduction to object tracking and practical pose estimation using human action recognition.
• Week 8: More advanced tracking techniques are covered, including tracking in crowds and dense environments.
• Week 9: Fundamentals of person recognition and soft biometric traits. Techniques for recognition by comparison and algorithm performance evaluation are introduced, along with real-world application.
• Week 10: Feature extraction and recognition using pattern analysis methods.
• Week 11: Continuation of Week 10, with various approaches for comparing extracted features.
• Week 12: Deep learning approaches for recognition in images, including specific models such as ResNet, EfficientNet, and ViT.
• Week 13: Continuation of person recognition using deep models and real-world solutions.
• Week 14: Students integrate detection and recognition components into a complete biometric pipeline. They are introduced to state-of-the-art methods and application examples.
• Week 15: Students learn the basics of bias, fairness, and ethics in AI and computer vision. They understand bias and fairness in image datasets, explore strategies for collecting and organizing unbiased image data, and implement techniques for data annotation. Multiple bias mitigation techniques are applied to existing datasets.

Masters Study, 2nd Cycle

Topical Research Themes 1 and 2

Analysis and Improvement of Business Processes

(Course is taught in Slovene.)

After completing the course, the student will:

• Know the theoretical aspects and practical limitations of different approaches in the framework of business process analysis and improvement.
• Know different approaches to business process improvement (requirements engineering, business information architecture modeling, decomposition, re-engineering, ...).
• Know how to use tools for capturing requirements (both functional and non-functional, business information architecture planning, modeling, ...).
• Know the engineering processes in the light of digital transformation (specifics of software engineering, specifics of service engineering, etc.).
• Know the processes of improving and innovating business processes.
• Know how to combine existing knowledge from the broader field of computer science and informatics and connect them with the specifics of digital transformation.
• Understand the practical application based on examples from use in industry.

Digital strategies and digital business models

(Course is taught in Slovene.)

The course Digital strategies and business models focuses on an in-depth study of modern digital strategies and digital business models. The course will examine how new disruptive information technologies enable digitalization of business processes, the design of digital products and services, and entering new markets. The aim of the course is to familiarize students with the key concepts of digital strategies and business models, as well as business, technical, organizational, managerial and social techniques that form the framework of the digital transformation of the company. This will empower them to independently prepare a digital strategy for the company and develop a plan for its implementation.

• Course Syllabus: Digital strategies and digital business models

Differentiable programming

(Course is taught in English.)

This course covers differential programming principles and techniques used in various fields like machine learning, computer vision, graphics, physics simulation, and scientific computing. Topics include program differentiation, optimization, and techniques like backpropagation and automatic differentiation. Also, differentiable physics and rendering, solving partial differential equations using neural networks, and differentiable convex optimization.