Admission requirements
Assumed prior knowledge
Elementary calculus and linear algebra; basics of probability theory and statistics; basics of machine learning. Fluency in Python; basic command of Linux.
Description
The course provides an introduction to key concepts, algorithms, and architectures for neural networks, with strong emphasis on Deep Learning and its application on computer vision / image processing. Topics include:
Deep neural network architectures
Optimization of deep networks
Convolutional neural networks and applications to image processing
Recurrent neural networks and transformers
Unsupervised and generative models
The course consists of weekly lectures, programming assignments (in Python, TensorFlow, Keras) and a final exam.
Course objectives
After this course, students are able to:
Describe and explain the key concepts of deep learning (CNNs, transformers, GANs, diffusion models, etcetera)
Apply deep learning algorithms to real-world problems in a group setting.
Create computer code that can train and apply deep neural networks.
Discriminate between deep learning algorithms and select which algorithm to use in which setting.
Evaluate the performance of deep learning models in real-world problems in a group setting.
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
Lectures
Computer Labs
Practical assignments (in a group setting)
Course load
Total hours of study 6 EC course: 168 hrs.
Assessment method
The final grade will be the weighted average of grades for:
programming assignments (60%)
exam (40%)
To pass the course, grades for both components should be at least 5.5. There is an opportunity to retake the exam. A retake of the programming assignments is available in the form of a single larger retake assignment.
The teacher will inform the students how the inspection of and follow-up discussion of the exams will take place.
Reading list
Understanding Deep Learning: https://github.com/udlbook/udlbook/releases/download/v2.04/UnderstandingDeepLearning_04_03_24_C.pdf
Dive into Deep Learning: https://d2l.ai/
Registration
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
Lecturer: dr. D. M. Pelt
Email: idl@liacs.leidenuniv.nl
Remarks
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.