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Seminar Multimedia and Deep Learning


Admission requirements

Not applicable.


(Additional details and up-to-date information can be found on the main course website -

Modern deep learning has its core and inspiration in the well known automatic image classification problem (what is in this picture?). The initial deep convolutional neural networks (e.g. AlexNet and ImageNet) had remarkable success over the previous generation of techniques. Since then, the fundamental ideas both evolved in breadth and scope and also have been applied to a much wider set of data (also text, MRI, audio, etc.) with major successes in diverse fields.

This course examines the fundamental and important ideas in deep learning on multimedia data by using both programming assignments and the scientific seminar format where in roughly half of the course there are scientific critical discussions on the important ideas, advances and scientific papers.

The discussions will cover the strengths and weaknesses, challenges and issues and future directions of computer vision and deep learning as methods of understanding diverse multimedia data.

The student must have moderate knowledge in C and C++ programming (Python is also useful) and should have an introductory level in image processing.

Course objectives

At the end of the Multimedia and Deep Learning course, the student should be able to

  • understand the fundamental principles of multimedia and deep learning systems.

  • analyze a multimedia deep learning system with regard to strengths and weaknesses and potential areas for improvements.

  • have insight into traditional and state-of-the-art multimedia deep features.

  • have insight into traditional and state-of-the-art multimedia deep learning algorithms.

  • have insight into scientifically evaluating a multimedia and deep learning systems.

  • have insight into the integration of intelligent algorithms into the analysis and retrieval process.

  • have insight into the limits and challenges of modern multimedia and deep learning systems.

  • develop and write scientific reports

  • develop and give scientific presentations

  • assess and evaluate the scientific credibility, rigor and reproducibility of deep learning and multimedia articles


The most recent timetable can be found at the main course website (link at the bottom of this webpage) 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

  • lectures

  • seminar

  • student discussions

  • presentations

  • homework and software assignments

Total hours of study: 168 hrs.
Lectures 20:00 hrs.
Programming and Homework: 80:00 hrs.
Student Presentations and Class Discussion: 50:00 hrs.
Other 18:00 hrs.

Assessment method

The final grade is composed of (1) 50% for Paper Presentation/Seminar, Class Participation & Questions & Assignments. (2) 50% for Final Project.

Assignments turned in late: grade penalty of -1 per 24 hours (1 day)

Source code for assignments must include instructions for compiling and execution in the machines in LIACS student computer rooms. This is necessary for grading/evaluating the work by the class organizers.

Additional details can be found on the main course website:

As this is a seminar, attendance (and class participation) is mandatory.

Reading list

  • Research papers from recent ACM conferences and journals


Please contact the teacher before registering. Because this is a seminar, there is a limit to the number of students allowed.

From the academic year 2022-2023 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.


Lecturer: Prof. Michael Lew
Website: Multimedia and Deep Learning