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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.

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.

    Assessment

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

    Examiner

    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.

  • Course literature

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