Parkinson’s disease (PD) is a progressive neurodegenerative disorder in which early detection remains a major clinical challenge due to heterogeneous motor and non-motor manifestations and the lack of reliable biomarkers. In recent years, machine learning (ML) and deep learning (DL) methods have been increasingly investigated as decision-support tools for PD screening using diverse clinical and behavioral data. This review synthesizes PD detection studies published between 2017 and 2025, systematically analyzing 32 representative works across multiple modalities, including MRI, PET, EEG, REM sleep biomarkers, voice recordings, gait signals, handwriting/drawing tasks, and finger-tapping measurements. Across the reviewed literature, high classification performance is frequently reported, with CNN-based and hybrid DL architectures achieving particularly strong results in imaging and time-series settings, while classical ML approaches such as SVM and ensemble models remain competitive for engineered feature-based datasets. However, the review also reveals major barriers to reliable translation, including small datasets, inconsistent evaluation protocols, limited external validation, and the risk of performance inflation caused by non-subject-independent data splitting. Overall, this review provides a structured and modality-oriented reference of algorithms, datasets, and performance trends, while highlighting key methodological gaps and practical priorities for developing robust and clinically deployable PD detection systems.

In this paper, we describe the possibility of using large language models (LLMs) in education through the development of a modern web system. A web-based platform employing LLMs to produce questions and answers spanning diverse domains, drawing upon Serbian Wikipedia as its primary knowledge repository. Leveraging LLMs for question-and-answer generation represents a novel methodology in quiz development. Additionally, incorporating Wikipedia enhances the accuracy and pertinence of the generated questions, thereby enriching the overall user experience.

Student hackathons have emerged as a dynamic educational mechanism for fostering creativity and accelerating the adoption of new technologies among Computer Science and Software Engineering students. First introduced in university settings in the early 2000s, hackathons have evolved into highly engaging events where students compete to develop innovative software or hardware solutions within a limited time frame. The competitive spirit inherent to hackathons resonates strongly with today's engineering students, motivating them to push boundaries, solve real-world problems, and collaborate effectively under pressure. Participation in hackathons enhances students' technical skills, promotes teamwork, and deepens their understanding of modern tools, frameworks, and platforms not always covered in formal curricula. Over the past five years, hackathons have gained remarkable popularity in Serbia, especially at state faculties and through active student organizations. Many of these events are organized in partnership with globally recognized ICT companies, providing students with valuable exposure to industry standards and expectations. At the University of Belgrade, the students' programmers' club, within the Google Developer Group on Campus program, has successfully hosted numerous hackathons that have become key platforms for skill development and innovation. These events not only contribute to academic growth but also significantly enhance students' employability and entrepreneurial thinking. Through hands-on experience and interaction with industry mentors, participants often gain clarity about their professional interests and career paths. Hackathons thus serve as bridges between university education and real-world software engineering practice, preparing students for both modern studies and the tech industry.