Machine Learning (ML)

Course content

The amount and complexity of available data is steadily increasing. To make use of this wealth of information, computing systems are needed that turn the data into knowledge. Machine learning is about developing algorithms for analysing data for making predictions, categorizations, and recommendations. Machine learning algorithms are already an integral part of today's computing systems - for example in search engines, recommender systems, or biometrical applications. Machine learning provides a set of tools that are widely applicable for data analysis within a diverse set of problem domains such as data mining, search engines, digital image and signal analysis, natural language modeling, bioinformatics, physics, economics, biology, etc.

The purpose of the course is to introduce students to the basic theory and most common techniques of statistical machine learning. The students will obtain a working knowledge in statistical machine learning.

This course is relevant for computer science students as well as for students from others studies with sufficient mathematical background and programming skills (e.g., Bioinformatics, Physics, Mathematics, Statistics, Mathematics-Economics, …) .

The course covers the following tentative topic list:

  • Foundations of statistical learning.
  • Parametric and non-parametric learning approaches.
  • Classification methods, such as: Linear models, K-Nearest Neighbor, kernel-based methods (e.g., support vector machines), and neural networks.
  • Regression methods, such as: Linear regression, non-linear regression.
  • Clustering.
  • Dimensionality reduction and visualization techniques such as principal component analysis (PCA).

MSc Programme in Computer Science
MSc Programme in Bioinformatics


Learning outcome

At course completion, the successful student will have:

Knowledge of

  • the general principles of machine learning;
  • basic probability theory for modeling and analyzing data;
  • the theoretical concepts underlying classification, regression, and clustering;
  • the mathematical foundations of selected machine learning algorithms;
  • common pitfalls in machine learning.


Skills in

  • applying linear and non-linear techniques for classification and regression;
  • performing elementary dimensionality reduction;
  • elementary data clustering;
  • implementing selected machine learning algorithms;
  • visualizing and evaluating results obtained with machine learning techniques;
  • using software libraries for solving machine learning problems;
  • identifying and handling common pitfalls in machine learning.


Competences in

  • recognizing and describing possible applications of machine learning;
  • comparing, appraising and selecting machine learning methods for specific tasks;
  • solving real-world data mining and pattern recognition problems by using machine learning techniques.

Lecture and exercise classes.

See Absalon when the course is set up.

Knowledge of and experience in programming is required. Participants must be able to implement algorithms described in pseudo code.

Knowledge of linear algebra corresponding to an introductory undergraduate course on the topic is expected (in particular: vector spaces; matrix inversion; eigenvalue decomposition; linear projections). This knowledge can be acquired/refreshed using any introductory book on linear algebra (e.g., Gilbert Strang, "Introduction to Linear Algebra").

Knowledge of basic calculus at an advanced high-school level is also expected (in particular: rules of differentiation; simple integration). This knowledge can be acquired/refreshed using any introductory book on calculus (e.g., Stephen Abbott, "Understanding Analysis"; Michael Spivak, "The Hitchhiker's Guide to Calculus"). There is a free online textbook and course "Calculus" by Gilbert Strang available at MIT OpenCourseWare, . The most relevant chapters/sections in this book are 1-3.4, 4.1, 5-6.4, 10, 11, and 13.

Knowledge of basic statistics and probability theory is a plus (in particular: discrete and continuous random variables; independence of random variables and conditional distributions; expectation and variance of random variables; central limit theorem and the law of large numbers). This knowledge can be acquired/refreshed using any introductory book on these topics (e.g., Sheldon Ross, "A First Course on Probability Theory", in particular the first six chapters). There is a free online course "Introduction to Probability and Statistics" by Jeremy Orloff and Jonathan Bloom available at MIT OpenCourseWare, , in particular the first part "Probability" is relevant.

Participants with weaknesses in one or more of the above areas should be prepared to spend some extra study time on their own, either before or during the course.

The course is mandatory for Computer Science students. Students from other study programs that do not have the necessary math and programming prerequisites are advised to check the "Introduction to Data Science" course, which is a math-light and programming-light alternative focused on applications of machine learning.

7,5 ECTS
Type of assessment
Written assignment, 7 days
One written take-home assignment.
All aids allowed
Marking scale
7-point grading scale
Censorship form
External censorship
Criteria for exam assessment

See Learning Outcome.

Single subject courses (day)

  • Category
  • Hours
  • Lectures
  • 28
  • Preparation
  • 14
  • Practical exercises
  • 57
  • Theory exercises
  • 57
  • Exam Preparation
  • 25
  • Exam
  • 25
  • English
  • 206