Advanced Machine Learning, 6 credits (732A96)

Advanced Machine Learning, 6 hp

Course description

The course covers advanced topics in machine learning, primarily from Bayesian perspective. A major part of the course is devoted to the graphical models, such as Hidden Markov Models, Bayesian networks, Markov random fields and other methods. The course includes laboratory works in which students get a practical experience of data analysis.

Main field of study



Second cycle

Course type

Single subject and programme course


Jose M Pena

Course coordinator

Jose M Pena

Director of studies or equivalent

Jolanta Pielaszkiewicz

Available for exchange students



Jolanta Pielaszkiewicz

Kostas Mitropoulos, international coordinator

Course offered for Semester Weeks Language Campus VOF
Single subject course (Half-time, Day-time) Autumn 2021 v202135-202143 English Linköping
Single subject course (Half-time, Day-time) Autumn 2021 v202135-202143 English Linköping
F7MSL Statistics and Machine Learning, Master´s Programme - First and main admission round 3 (Autumn 2021) v202135-202143 English Linköping v
F7MSL Statistics and Machine Learning, Master´s Programme - Second admission round (open only for Swedish/EU students) 3 (Autumn 2021) v202135-202143 English Linköping v

Main field of study


Course level

Second cycle

Advancement level


Course offered for

  • Master's Programme in Statistics and Machine Learning

Entry requirements

  • Bachelor's degree equivalent to a Swedish Kandidatexamen of 180 ECTS credits in one of the following subjects:
    • statistics
    • mathematics
    • applied mathematics
    • computer science
    • engineering
  • Passed courses in
    • calculus
    • linear algebra
    • statistics
    • programming
  • Passed course in Bayesian Learning of at least 6 ECTS credits
  • Passed course in Computational Statistics of at least 6 ECTS credits
    or equivalent
  • English corresponding to the level of English in Swedish upper secondary education (Engelska 6/B)
    (Exemption from Swedish)

Intended learning outcomes

After completion of the course the student should on an advanced level be able to:

  • account for the principles of machine learning used in the Bayesian tradition of machine learning,
  • construct a suitable probabilistic model describing the data structure and the prior,
  • compare between models in order to select the best one,
  • implement machine learning models in a programming language and also use standard machine learning libraries in order to perform the model inference, make predictions based on these models and estimate the uncertainty of these predictions.

Course content

The course covers some advanced methods in machine learning that allow for modelling complex phenomena and predicting the outcomes of these phenomena. 

The following topics are included in the course:

  • Introduction to Bayesian Learning: likelihood, prior, posterior, marginal likelihood, posterior predictive distribution. Generative and discriminative models,
  • Gaussian process,
  • State-space models,
  • Kalman filtering and smoothing,
  • particle methods,
  • Markov models and hidden Markov models,
  • graphical models, such as Bayesian networks and Markov random fields.

Teaching and working methods

The teaching comprises lectures, seminars, and computer exercises complemented by self-studies. Lectures are devoted to presentations of theories, concepts and methods. Computer exercises provide practical experience of data analysis in some machine learning software. The seminars comprise student presentations and discussions of computer assignments.
Language of instruction: English.


Written reports on the computer assignments. Active participation in the seminars. One final written examination. Detailed information about the examination can be found in the course’s study guide.

If the LiU coordinator for students with disabilities has granted a student the right to an adapted examination for a written examination in an examination hall, the student has the right to it. If the coordinator has instead recommended for the student an adapted examination or alternative form of examination, the examiner may grant this if the examiner assesses that it is possible, based on consideration of the course objectives.

Students failing an exam covering either the entire course or part of the course twice are entitled to have a new examiner appointed for the reexamination.

Students who have passed an examination may not retake it in order to improve their grades.



Other information

Planning and implementation of a course must take its starting point in the wording of the syllabus. The course evaluation included in each course must therefore take up the question how well the course agrees with the syllabus. 

The course is carried out in such a way that both men´s and women´s experience and knowledge is made visible and developed.


Institutionen för datavetenskap
There is no course literature available for this course.
DAT1 Examination EC 3 credits
LAB1 Laboratory work EC 3 credits

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