Machine Learning Use-case Laboratory

The key issue in the utilization of machine learning and data science knowledge is that we can properly map a real data set and an actual business problem to our machine learning and data analysis toolset. The aim of the subject is to give students a deeper practical experience in this data analysis process by solving several real case studies to demonstrate the order and manner in which the methods should be used. From a methodological point of view, during the lab, the students create their own Notebook at the same time as the lecturer. In order to be able to concentrate on advanced task-solving tasks, we start with further development of an initial Notebook during the occasions. The semester also includes a designated data mining ladder competition, where you have to solve a supervised machine learning task as efficiently as possible.

The students meet the lecturers 10 times, each time in a four-hour time slot, where assignments are issued every two weeks (biweekly). In addition, the students have to solve a large homework task during the semester, where they have to compete with each other (data science competition).

After each lesson, an example solution for the analysis tasks will be published.

Theoretical foundations of machine learning, basic knowledge of programming, basic knowledge of probability calculations

Requirements: - Assignments (small homeworks) to be submitted within the Moodle system (5 pieces) - assignments are issued every two weeks, two weeks are available to complete the assignment. - Participation in the data science competition (large homework) related to the subject, where the baseline specified during the semester must be reached. Small homework scores count for 40%, while the data science competition scores count for 60% of the grade. At the competition, a baseline level must be reached in order for the student to receive at least a pass (2) grade, this level is recorded when the competition is announced. During the competition, a supervised learning task must be solved, the exact metric is determined by the competition task announced in the given semester. The students start the competition independently, the available score depends on the results achieved by all the students participating in the competition in the given year. At least 40% of the total score (small homeworks plus competition) must be achieved to receive credit. Additional possibilities: Assignments (small homeworks) to be submitted can be made up 2 weeks after the submission deadline. If this deadline extends beyond the teaching period, they must be submitted in one week after the teaching period. The submission of the large homework (competition) is continuous from the middle of the semester, the submission cannot be replaced here, the competition closes at the end of the teaching period.