Machine learning project 1000-2M19MLP
The course will have various lectures on:
- How does the work on a scientific project look like?
- How does the publication process of scientific results look like? How to write a paper, prepare a presentation and a poster, and how to give an elevator pitch?
Some of the lectures may be conducted by invited guests, subject to their availability.
During the first weeks various research topics and mentors will be presented. Students will select their topics and mentors, and define research goals. Then, students will define concepts and start working on research projects. At the end of the course, students will present demos, work on paper drafts, and finally give presentations.
We dedicate the course to 3rd-5th year students. We require initial good knowledge and experience with Machine Learning / Deep Learning. Proficiency in programming.
The subject is research-oriented. Participation will require quite a lot of effort (about 15 hours of work per week).
Type of course
Requirements
Prerequisites (description)
Learning outcomes
Have an understanding and experience on how the scientific work look like.
Assessment criteria
The grade will be given by the course coordinator based on:
- mentor evaluation,
- progress reports and presentations.
Participation in both lectures and laboratories is mandatory.
Bibliography
Research papers on studied topics.
Additional information
Information on level of this course, year of study and semester when the course unit is delivered, types and amount of class hours - can be found in course structure diagrams of apropriate study programmes. This course is related to the following study programmes:
- Bachelor's degree, first cycle programme, Computer Science
- Master's degree, second cycle programme, Computer Science
Additional information (registration calendar, class conductors, localization and schedules of classes), might be available in the USOSweb system: