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Machine Learning

The course addresses the question of how to enable computers to learn from previous experiences. 

First, the machine learning field is introduced, describing a variety of learning paradigms, algorithms, theoretical results and applications. Then basic concepts from statistics, information theory and probability theory are introduced to the degree they are relevant to machine learning. 

The course deals with advanced models such as deep learning and focuses on both theory and applications of these models. The course covers ethical aspects of machine learning models, model prejudices at different levels and how they can be handled in an effective way.

  • Course structure

    The language of instruction is English.

    Teaching format

    The teaching activities in the course are: lectures and exercises.

    Assessment

    The course is examined through assignments and a written examination.

    Examiner

     
  • 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.

  • Contact

    This course is part of a programme, course package or incoming exchange studies and is not available for application as a stand-alone course.

    Find more information about Master’s programmes here

    Student counsellor - master