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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.

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.


  • You have to sign up for courses and exams (including retakes) in uSis. Check this link for information about how to register for courses.


Dr. Anna V. Kononova Diederick Vermetten