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.
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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
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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.
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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
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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.
