This seminar is designed for students with basic knowledge of machine learning who seek to explore and apply ML techniques to solve real-world challenges. The course emphasizes a research-oriented, project-based approach, guiding students from problem definition to solution implementation, including analysis, model training, and evaluation. The course will culminate in student presentations and research papers documenting their projects, reflecting on the practical and theoretical contributions of their work.
During the course, methods for image processing and computer vision will be presented, with an emphasis on techniques currently used in academia and industry. Topics will include basic image processing, feature detection of objects in images, image quality enhancement, object tracking and classification, color identification, depth, lighting, and shading, scene understanding in images, and more.
This course introduces students to the fundamental principles of machine learning, focusing on understanding and applying various algorithmic models. Students will learn how to analyze and interpret complex datasets, build regression and classification models, and apply unsupervised learning techniques. Throughout the course, students will be assigned homework exercises to practice the concepts learned, and a final exam will be held