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Introduction to machine learning

Yksittäinen kurssi

Max amount of FITech students: 10

Persons without a valid study right to a Finnish university have preference to this course.

The course introduces the basic principles of machine learning as well as tools and methods to apply machine learning in practice.

The course has a short prerequisite knowledge test – available on the course website – which contains a more detailed description of the required prerequisites and pointers to self-study materials. The courses Introduction to Data Science and Introduction to Artificial Intelligence are recommended but not required.

Course contents

  • Ingredients of machine learning: components (tasks, computational problems, algorithms etc.) and necessary tools.
  • Introduction to statistical learning and probabilistic modelling.
  • Supervised learning: basic definition, basic regression and classification algorithms (linear probabilistic, distance based models).
  • Statistics and evaluation: estimating parameters and resampling methods (including validation set methods).
  • Unsupervised learning: clustering methods (k-means, agglomerative clustering) and basics of dimensionality reduction (PCA and variants).

Learning objectives

Machine learning (ML) is the core technology under the recent developments of artificial intelligence (AI) and it is applied widely in several domains. This course will provide you with the necessary theoretical background to understand the fundamental machine learning concepts and to use the basic methods of supervised and unsupervised learning in a proper manner to solve real-life problems. The course will prepare you for the further studies in machine learning and introduce you to the methods and tools that are used to solve the problems in practice.

Course material

  • Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani: An Introduction to Statistical Learning with Applications in R, Springer, 2017.
  • Additional readings are announced during the course.

Parts of the textbook that are required are specified on the course webpage.

Completion methods

  • Lectures WE 12-14, FR 10-12 (material will be available after the lectures)
  • Exercises MO 14-16 or MO 16-18

Assessment and grading is based on completed exercises and term project. There is no exam. Possible other criteria will be specified on the course webpage.

More information on the University of Helsinki’s course page.

You can get a digital badge after completing this course.


Helsingin yliopisto
Kai Puolamäki

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Fanny Qvickström, Opintoasioiden suunnittelija
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Kuka voi hakea:
Generic skills learned during BSc studies, including writing of academic reports. High school and university mathematics, including basics of optimization with differentiation. Linear algebra, including basic matrix and vector operations, eigenvalues, and eigenvectors. Probability and statistics, including random variables, expectation, and rules of probability. Programming skills, some programming experience, and ability to quickly acquire the basics of a new environment such as R or Python. Additionally, it is useful to know the basic ideas of pseudocode and the analysis of time and space complexity with big O notation.
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