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.
In MyTimetable, you can find all course and programme schedules, allowing you to create your personal timetable. Activities for which you have enrolled via MyStudyMap will automatically appear in your timetable.
Additionally, you can easily link MyTimetable to a calendar app on your phone, and schedule changes will be automatically updated in your calendar. You can also choose to receive email notifications about schedule changes. You can enable notifications in Settings after logging in.
Questions? Watch the video, read the instructions, or contact the ISSC helpdesk.
Note: Joint Degree students from Leiden/Delft need to combine information from both the Leiden and Delft MyTimetables to see a complete schedule. This video explains how to do it.
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.
As a student, you are responsible for enrolling on time through MyStudyMap.
In this short video, you can see step-by-step how to enrol for courses in MyStudyMap.
Extensive information about the operation of MyStudyMap can be found here.
There are two enrolment periods per year:
Enrolment for the fall opens in July
Enrolment for the spring opens in December
See this page for more information about deadlines and enrolling for courses and exams.
Note:
It is mandatory to enrol for all activities of a course that you are going to follow.
Your enrolment is only complete when you submit your course planning in the ‘Ready for enrolment’ tab by clicking ‘Send’.
Not being enrolled for an exam/resit means that you are not allowed to participate in the exam/resit.
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.
Software
Starting from the 2024/2025 academic year, the Faculty of Science will use the software distribution platform Academic Software. Through this platform, you can access the software needed for specific courses in your studies. For some software, your laptop must meet certain system requirements, which will be specified with the software. It is important to install the software before the start of the course. More information about the laptop requirements can be found on the student website.