When the course guarantor is selected, comments will be displayed regarding his/her teaching performance as well as that of all the other teachers teaching the course. If a teacher who is not the course guarantor is selected, only comments related to his/her teaching will be displayed.
Mgr. Marta Vomlelová, Ph.D. [32-KTIML], Machine Learning [NAIL029, přednáška]
Student has disallowed display of personal data, 06.08.2025, 1. ročník, Mathematical Analysis, Master's (post-Bachelor)
Summer semester 2025. Prof. Vomlelova is highly knowledgeable and responsive, offering clear, insightful explanations that go beyond the course material. Although she seemed slightly hesitant early on, her confidence grew over time. Her patience and dedication to student learning are commendable, and with more vocal confidence, her teaching style could be even more impactful.
Professor Vomlelova is remarkably knowledgeable and quick-witted. She not only encourages questions but also extends her explanations beyond the course content, offering insightful answers that directly clarify the lecture material. Her inherent teaching qualities are commendable, and she should confidently showcase these qualities during her lectures.
While I initially sensed a hint of unease or hesitation in her presentation at the beginning of the semester, this gradually dissipated. I sincerely appreciate her dedication to delivering coherent lectures while patiently addressing our questions, no matter how basic they may have seemed, all in the spirit of fostering our learning.
I encourage her to continue growing in confidence and feel free to be louder, which would further enhance her teaching style. Overall, I am deeply grateful for the opportunity to have taken her classes and gained invaluable knowledge that has also significantly benefited my performance in other courses.
Comment on course, Machine Learning [NAIL029, přednáška]
Student has disallowed display of personal data, 06.08.2025, 1. ročník, Mathematical Analysis, Master's (post-Bachelor)
Summer semester 2025. An overall above average course: a well-organized one that provided a theory-focused overview of machine learning methods. While outdated slides were occasionally uploaded, they were corrected in time for the exams. I would also suggest to have better-organized slides: sometimes it was difficult to even understand the point of the topics illustrated. I would have also put more emphasis on the practical application and aspects of the methods taught during lectures.
he course content is well-organized, offering a comprehensive overview of machine learning methods heavily influenced by "The Elements of Statistical Learning." There were occasional instances where outdated slides were posted on Moodle, although these were promptly corrected before the exams. It was noted by some participants that this happened a few times, but it was not a persistent occurrence. In the future, I would appreciate more consistent updates of slides after each lecture to ensure current material is readily available. Despite these occasional issues, the course introduced me to new logical inductive reasoning techniques in machine learning and effectively integrated a variety of methods into a cohesive unit. Deep learning and deep reinforcement learning, which have dedicated courses, were appropriately excluded from this overview. Overall, the course provided a deep theoretical understanding and a unique perspective on machine learning.
