Admission requirements
Calculus
Algorithmics
Linear algebra
Programming with Python
Statistics for computer scientists (or taking it in parallel)
Description
Machine learning as a subfield of artificial intelligence (AI) is the science of the design of algorithms that can learn through experience acquired from data, without being explicitly programmed. This course provides an in depth overview of various topic in machine learning and provides knowledge on the core concepts needed by AI scientists who are not only going to apply machine leanring algorithms but also design new algorithms themselves. Throughout the course, students will get familiar with the fundamental concepts in the design of effective machine learning algorithms, and different classes of machine learning models. Different groups of machine learning algorithms and their innerworkings are discussed. The course also focuses on evalution of algorithms by providing an overview of various evaluation metric and descriving how to perform statistical analysis of the resutls. The students further gain the practical skills needed to apply machine learning algorithms to new problems.
Course objectives
After this course, the students are able to:
Characterise and differentiate between different classes of machine learning models (e.g., geometric, probabilistic);
Characterise and differentiate between different classes of machine learning tasks (e.g., classification, clustering, regression);
Describe how different machine learning algorithms work and are implemented;
Evaluate and assess the performance of different machine learning algorithms;
Formulate new machine learning problems and apply available algorithms to solve them.
Develop source code and perfrom machine learning experiments, evaluate and present the results and draw sound conclusions.
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
Not applicable.
Assessment method
Written examination with closed questions (70%)
Assignments (30%)
The final grade for the course is established by determining the weighted average. However, the grade of the exam should be above the passing margin (5.5). There is an opportunity to retake the exam, but not the assignments.
The teacher will inform the students how the inspection of and follow-up discussion of the exams will take place.
Reading list
Flach, Peter., Machine learning: the art and science of algorithms that make sense of data, Cambridge University Press, 2012.
Other reading material are provided per lecture.
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
Education coordinator LIACS bachelors
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.