nl en

Introduction to Machine Learning


Admission requirements

Not applicable.


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

  1. Provide introduction to Machine Learning techniques (via lectures, exercises and assignement)
  2. Develop practical skills of applying Machine Learning techniques (via exercises and assignment)
  3. Develop skills of scientific reporting (via assignment report)


The most recent timetable can be found at 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 (including several practicums)

  • programming assignment including a report

  • homework

  • written exam

Course load

Total hours of study 6 EC course: 168h
Lectures/Workgroups: 32:00 hrs.
Homework: 24:00 hrs.
Assignment: 68:00 hrs.
Self-study: 44:00 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.


Every student has to register for courses with the new enrollment tool MyStudyMap. 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.


Dr. Anna V. Kononova Diederick Vermetten