Stockholm university
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Machine learning

In this course you will learn how to formulate and organize solutions to practical machine learning problems, identify and estimate appropriate machine learning models for prediction and clustering, evaluate and select among different machine learning models and algorithms and implement machine learning models and algorithms in a programming language.

Information for admitted students Autumn 2023

Congratulations! You have been admitted at Stockholm University and we hope that you will enjoy your studies with us.

In order to ensure that your studies begin as smoothly as possible we have compiled a short checklist for the beginning of the semester.

Follow the instructions on whether you have to reply to your offer or not.


Checklist for admitted students

  1. Activate your university account

    The first step in being able to register and gain access to all the university's IT services.

  2. Register at your department

    Registration can be done in different ways. Read the instructions from your department below.

  3. Read all the information on this page

    Here you will find what you need to know before your course or programme starts.


Your seat may be withdrawn if you do not register according to the instructions provided by your department.

Information from your department

On this page you will shortly find information on registration, learning platform, etc.

Welcome activities

Stockholm University organises a series of welcome activities that stretch over a few weeks at the beginning of each semester. The programme is voluntary (attendance is optional) and includes Arrival Service at the airport and an Orientation Day, see more details about these events below.
Your department may also organise activities for welcoming international students. More information will be provided by your specific department. 

Find your way on campus

Stockholm University's main campus is in the Frescati area, north of the city centre. While most of our departments and offices are located here, there are also campus areas in other parts of the city.

Find your way on campus

Read more

New student

During your studies

Student unions

For new international students

Pre-departure information

New in Sweden

The course gives you knowledge about machine learning that is used within marketing, finance, economics, textual analysis, digital humanities and social scienses.

The course covers a number of machine learning methods with a focus on prediction. The course deals with supervised and unsupervised machine learning as well as semi-supervised and active learning. The course includes flexible regression and classification, regularization, methods for predictive model performance evaluation, Gaussian processes, clustering algorithms and mixture models.

  • Course structure

    The course is given at day time, full time.

    The course forms a part of the Master's Program in Statistics, but it can also be studied as a freestanding course.

    Teaching format

    The instruction consists of lectures and computer labs. The course is taught in English.

    Course Information

    All courses at the Department of Statistics will be given online during the spring semester 2021.

    More information for registered students will be found in Athena.


    Examination will be in the form of a written test and a written hand in group assignment.


    Teachers Spring 2021

    Mattias Villani

    Frank Miller

    You will find the teacher's reception hours in the link above. If you want to visit your teacher outside of the reception hours, you are welcome to e-mail for an appointment.

  • Schedule

    The schedule will be available no later than one month before the start of the course. We do not recommend print-outs as changes can occur. At the start of the course, your department will advise where you can find your schedule during the course.
  • Course literature

    Note that the course literature can be changed up to two months before the start of the course.
  • Course reports

  • Contact

    Teachers Spring 2021

    Course coordinator

    Mattias Villani

    Frank Miller

    You will find the teacher's reception hours in the link above. If you want to visit your teacher outside of the reception hours, you are welcome to e-mail for an appointment.

    If you have questions about studying at the Department of Statistics, please contact our study- and career counselor: Jenny Rosen.