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Introduction to Machine Learning


Admission requirements

Students are assumed to have good understanding of mathematics including elementary calculus, linear algebra, basic probability and statistics and be fluent in Python and latex.


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

The course consists of weekly lectures, two programming assignments (in Python, with the scikit-learn library) that have to be submitted accompanied by a report. The course has written exam.

Course objectives

Concise description of the course objectives formulated in terms of knowledge, insight and skills students will have acquired at the end of the course. The relationship between these objectives and achievement levels for the programme should be evident.
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 on the student’s website

Mode of instruction

  • lecture

  • programming assignments

  • report

  • written exam

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, all grades should be at least 5.5.

The teacher will inform the students how the inspection of and follow-up discussion of the exams will take place.

Reading list

Slides contain all necessary material covered by this course. List of additional reading material can be provided in the slides for some lectures.


Please register via Usis.


Dr Anna Kononova
Diederick Vermetten