Admission requirements
Assumed prior knowledge
It is assumed that the student has
Good programming skills in Python (see for example the content of the course 'Algorithms and Data structures')
Good knowledge of Data Science and Machine Learning techniques (see for example the content of the courses 'Data Science', 'Machine Learning' and 'Symbolic AI')
Familiarity with deep learning (see 'Introduction to Deep Learning'--could be followed in parallel)
Description
The fields of Data Science and Machine Learning deal with large volumes of data. Complex algorithms such as stochastic gradient descent, gradient boosting and support vector machines are able to model this data and make predictions about future trends. Most of these algorithms have a high number of hyperparameters, that need to be tuned correctly for the resulting model to perform well. Properly tuned hyperparameters can determine the difference between mediocre performance and state-of-the-art performance. When presented with a new dataset, common problems that need to be addressed are: Which algorithm to use and how to tune the hyperparameters to obtain good predictive performance. The research field of Automated Machine Learning (AutoML) focuses on how to automate this process.
Course objectives
There will be several lectures presented by the lecturers (approximately 6) in which core techniques from the field of AutoML are presented. The other lecture slots are filled with student presentations and discussions. Students will work in small teams (exact size to be determined), analyzing and understanding a seminal research paper and presenting it to the other students. Furthermore, the students will perform various research assignments to gain hands-on knowledge of state-of-the-art techniques in the field of AutoML. At the end of the course, the student should be able to:
Understand the various aspects of AutoML (e.g., search space, search algorithm, evaluation mechanism and the combination of these)
Understand the various problem definitions that are commonly solved by AutoML techniques
Analyze state-of-the-art hyperparameter optimization techniques, including (but not limited to) Bayesian optimization and hyperband.
Apply state-of-the-art AutoML tools on novel problem instances (e.g., using a convolutional neural network or gradient boosting on a new image dataset)
Apply various meta-learning and transfer learning techniques (e.g., MAML, Reptile, matching networks, memory-augmented neural network)
Evaluate relevant AutoML papers
Disseminate obtained knowledge in a comprehensible way (using the following form: presentation)
See Bloom’s taxonomy for a further explanation of the required level of understanding per item.
Timetable
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 (content will be provided by the lecturers)
Seminar (content will be provided through student presentations & discussions)
Research programming assignments
Exam
Course load
Total hours of study: 168 hrs. (= 6 EC)
The time will be divided between:
Preparing the lectures
- weekly: read the paper that will be presented
- once: prepare to present a paper
Attending the lectures
Research assignments
- programming / designing experiments/report writing
Exam (preparation + taking the exam)
Group work is an integral part of the course. You will be expected to complete the assignments together with a team mate.
Assessment method
The weighting of the final grade will be:
20% presentation about a state-of-the-art AutoML paper. Special emphasis is given to being able to disseminate obtained knowledge in a comprehensible way to fellow-students
30% research assignments in which relevant AutoML problems are solved
50% final exam
The minimal grade per component is 1.0. Each of the components needs to be completed with a passing grade (higher or equal to a 5.5), to complete the course with a passing grade.
Reading list
Recent and seminal papers from the AutoML literature.
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
Lecturers: dr. J.N. van Rijn
Remarks
More information will be available on the dedicated website for this course, hosted on BrightSpace.
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.