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.
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.
Information for admitted students autumn 2024
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.
universityadmissions.se
Checklist for admitted students
-
Activate your university account
The first step in being able to register and gain access to all the university's IT services.
-
Register at your department
Registration can be done in different ways. Read the instructions from your department below.
-
Read all the information on this page
Here you will find what you need to know before your course or programme starts.
IMPORTANT
Your seat may be withdrawn if you do not register according to the instructions provided by your department.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.
Read more
For new international students
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
-
Course description fall 2023 (223 Kb)
More information for registered students will be found in Athena.
Assessment
Examination will be in the form of a written test and a written hand in group assignment.
Examiner
Teachers Fall 2023
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
Teacher Fall 2023
Course coordinator
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.
