Due to the format of the course, the maximal number of students that can participate in this course is limited to 30. Students should also sign up for the course via uSis before 10 January 2022. Various articles, reports, conference proceedings. The teacher will inform the students how the inspection of and follow-up discussion of the exams will take place. Details of will be announced at the beginning of the course. The final grade will depend on student's participation in discussions, quality of presentations, the final report. Lectures, discussions, feedback on students presentations and reports. The most recent timetable can be found at the Computer Science (MSc) student website. Learn to work together is small research teams, Gain some hands-on research experience, including studying related papers, identifying research problems, inventing solutions of these problems, verifying these idea by experimenting and documenting findings in a scientific style, Identify some promising research directions Gain an overall picture of the recent developments in Deep Learning, Each team will have to summarize their work in a final presentation and a project report. The best reports can be submitted to conferences or published as research papers.ĭuring the course, after a few introductory lectures, students will work (in small teams) on selected topics/problems, performing experiments on GPU-computers (if applicable), reporting on their progress during weekly meetings. This research will have a form of producing new experimental results, developing new algorithms or theories and documenting findings in scientific reports. The main objective of this course is to provide a wide overview of the current state of this area and to focus on a few, carefully selected topics, covering them in depth by studying and presenting most relevant papers, and doing own research on these selected topics. In recent years we witness an explosion of research, development, and applications of Deep Learning. You must have completed the course Introduction to Deep Learning 2020-2021 or Deep Learning and Neural Networks 2019-2020 with a grade of at least 8.5 or pass an equivalent course elsewhere. Admission requirements Assumed prior knowledge
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