Admission requirements
Not applicable.
Description
This course equips students with foundational understanding of key concepts of Machine Learning (ML) and demonstrates how to solve real world problems with ML techinques. It covers the following topis:
Learning Theory
Supervised Learning
Unsupervised Learning
Transfer and Ensemble Learning
Course objectives
The course gives a comprehensive introductory overview of the field through a series of lectures and exercises. In addition, weekly homeworks and a practical application exercise of algorithms are given to the students, who are expected to run experiments and write a short report about the experiment and the results obtained.
By attending the course, students learn to:
understand how the concept of learning can be translated for the use by computers, understand general machine learning concepts like coefficient of determination, over- and underfitting, stochastic gradient descent, kernel trick, loss function, error measure, learning curve
be able to formalise the problem of learning in the setting of statistical learing theory including PAC learnability, regularisation, model validation and model selection
compare the working principles of various supervised and unsupervised machine learning methods
be able to use effectively methods such as support vector machines, random forests, decision trees, ensemble learning with bagging and boosting
be able to apply machine learing methods for timeseries, explainable AI and optimisation
motivate the choice of ML method for a given problem
develop working code of instances of ML methods discussed in the course
apply instances of ML methods to a task inspired by a real-word problem
create a scientific report that describes ML methods, analyses and evaluates their 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
lectures (including several practicums)
programming assignment including a report
homeworks
written exam
Course load
Total hours of study 6 EC course: 168h
Lectures and workgroups: 36:00 hrs.
Homework: 24:00 hrs.
Assignment: 70:00 hrs.
Self-study: 38:00 hrs.
Assessment method
The final grade is a weighed average of grades for:
the practical assignment that consists of python implementation, report produced via latex and peer review (30%)
the weekly homework assignments (10%)
the written examination with a mixture of multiple choice questions and questions with short free form answers (60%)
To pass the course, a grade of 5.5 or higher should be achieved for the exam, assignment and the weighted average.
Reading list
Slides contain all necessary material covered by this course. List of additional optional reading material can be provided in the slides for some lectures.
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
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.