Syllabus for Machine Learning

Maskininlärning

  • 7.5 credits
  • Course code: 2ST129
  • Education cycle: Second cycle
  • Main field(s) of study and in-depth level: Statistics A1F

    Explanation of codes

    The code indicates the education cycle and in-depth level of the course in relation to other courses within the same main field of study according to the requirements for general degrees:

    First cycle

    • G1N: has only upper-secondary level entry requirements
    • G1F: has less than 60 credits in first-cycle course/s as entry requirements
    • G1E: contains specially designed degree project for Higher Education Diploma
    • G2F: has at least 60 credits in first-cycle course/s as entry requirements
    • G2E: has at least 60 credits in first-cycle course/s as entry requirements, contains degree project for Bachelor of Arts/Bachelor of Science
    • GXX: in-depth level of the course cannot be classified

    Second cycle

    • A1N: has only first-cycle course/s as entry requirements
    • A1F: has second-cycle course/s as entry requirements
    • A1E: contains degree project for Master of Arts/Master of Science (60 credits)
    • A2E: contains degree project for Master of Arts/Master of Science (120 credits)
    • AXX: in-depth level of the course cannot be classified

  • Grading system: Fail (U), Pass (G), Pass with distinction (VG)
  • Established: 2021-10-15
  • Established by: The Department Board
  • Applies from: week 35, 2022
  • Entry requirements: 120 credits including 90 credits in statistics and 7.5 credits programming in R, Python or Julia.
  • Responsible department: Department of Statistics

Learning outcomes

After completing the course, the student is expected to:

  • have a good knowledge of a large number of machine learning models
  • be able to use methods for evaluating and improving predictive models
  • be able to describe and discuss ethical aspects of big data and black box-models
  • be able to handle big data
  • be able to train and use machine learning models in R
  • be able to train and use neural networks using Keras/Tensorflow

Content

Regularised regression, nearest neighbour methods, decision trees, ensemble models, bagging, out-of-sample evaluations, handling of big data, ethical questions regarding big data and predictive models, methods for explainable machine learning, and neural networks: architectures, gradient descent, generative models, regularisation and adversarial examples.

Instruction

Instruction is given in the form of lectures, labs and/or as seminars.

Assessment

The examination takes place through written and/or oral presentation of compulsory assignments.

Other directives

This course is part of the master degree program in statistics.

Reading list

The reading list is missing. For further information, please contact the responsible department.