Admission requirements
Assumed prior knowledge
It is strongly recommended that students:
have successfully completed Introduction to Deep Learning.
is interested in scientific research.
is proficient in programming, machine learning, presenting and report writing.
This is an advanced course. We will work intensively on state-of-the-art research topics.
Warnings:.
- Peer feedback is an important aspect of the course, and presence at the weekly meetings is mandatory.
Description
In recent years we have witnessed an explosion of research, development, and applications of Deep Learning. 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. This research will have a form of producing new experimental results, testing new algorithms or theories and documenting findings in scientific reports. The best reports can be submitted to conferences or published as research papers.
During the course students will work (in small teams) on selected topics/problems, performing experiments on GPU-computers, reporting on their progress during weekly meetings. Each team will have to summarize their work in a final presentation and a project report.
Course objectives
During the course students will:
Understand recent developments in Deep Learning, for instance transformers, foundation models, diffusion models
Analyse scientific papers in deep learning or related fields, e.g. computer vision, natural langauge processing, representation learning. Both understand the content of a paper and identify potential research directions which extend the work.
Adapt the code associated with an existing deep learning research paper implementing some of the potential research directions identified
Evaluate the impact of the new research directions by evaluating their performance on existing datasets.
Understand how to practice effective team work and apply this understanding to the group project
Understand how to give an effective presentation and apply this understanding to the final presentation summarizing the prior work, new research direction implemented and their evaluation
Create a scientific report summarizing the prior work, new research directions implemented and the evaluation of results.
Timetable
The most recent timetable can be found at the Computer Science (MSc) student website.
You will find the timetables for all courses and degree programmes of Leiden University in the tool MyTimetable (login). Any teaching activities that you have sucessfully registered for in MyStudyMap will automatically be displayed in MyTimeTable. Any timetables that you add manually, will be saved and automatically displayed the next time you sign in.
MyTimetable allows you to integrate your timetable with your calendar apps such as Outlook, Google Calendar, Apple Calendar and other calendar apps on your smartphone. Any timetable changes will be automatically synced with your calendar. If you wish, you can also receive an email notification of the change. You can turn notifications on in ‘Settings’ (after login).
For more information, watch the video or go the the 'help-page' in MyTimetable. Please note: Joint Degree students Leiden/Delft have to merge their two different timetables into one. This video explains how to do this.
Mode of instruction
Presentations, discussions, feedback on students presentations and reports.
Course load
Total hours of study: 168 hrs. (= 6 EC)
Lectures: 10 hrs
Discussions and presentations on student's project: 16 hrs
At home preparation: 142 hrs
Assessment method
The final grade will depend on student's participation in discussions, quality of presentations and the quality of the final report. Each team will receive a grade which is a rounded average of grades for the following components:
Project Report (70%)
Presentations (20%)
Active participation (10%)
The team's grade will be internally distributed among team members according to their individual contributions to the project.
To pass the course, the student needs to be present in at least 80% of the lectures. In case of a fail grade, there is an opportunity to resumbit the project report, but only with the same research project.
Reading list
The reading list changes every year according to recent developments in deep learning and the project topics chosen by students. See the course page of brightspace for more information.
Registration
Students should also sign up for the course before 15 January 2024. Due to the format of the course, the maximal number of students that can participate in this course is limited to 30. In case when more than 30 students would like to attend this course we will prioritize students on the Artificial Intelligence specialisation.
From the academic year 2023-2024 on every student has to register for courses with the new enrollment tool MyStudyMap. There are two registration periods per year: registration for the fall semester opens in July and registration for the spring semester opens in December. Please see this page for more information.
Please note that it is compulsory to both preregister and confirm your participation for every exam and retake. Not being registered for a course means that you are not allowed to participate in the final exam of the course. Confirming your exam participation is possible until ten days before the exam.
Extensive FAQ's on MyStudymap can be found here.
Contact
Lecturers: Dr. Hazel Doughty
Website: Brightspace
Remarks
Due to the format of the course, the maximal number of students that can participate in this course is limited to 30.
This course is also suitable/recommended to PhD students who want to use Deep Learning in their projects.