FIK3616 Learning Machines 7.5 credits

Lärande maskiner

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Course information

Content and learning outcomes

Course contents *

  • Foundations of AI for learning machines. (Default lecturer first year Magnus Boman)
  • History of learning machines. (Nina Wormbs)
  • The future of learning machines. (Magnus Boman)
  • TBC. (Anders Holst)
  • Pronouncers. (Magnus Boman)
  • Multi-AI (AI2AI) systems. (Magnus Boman)
  • Concept formation in learning machines. (Daniel Gillblad)
  • Deep learning. (John Ardelius)
  • Systemic properties of large-scale learning machines. (Daniel Gillblad & Magnus Boman)
  • Critical perspectives and fear of learning machines. (Francis Lee)
  • Massive data for learning machines. (Jim Dowling)
  • Applications of learning machines. (Magnus Sahlgren & Jussi Karlgren)
  • Learning from failure in combinatorial problem solving. (Christian Schulte)

Intended learning outcomes *

  1. Autonomously solving problems
    Applying existing as well as future tools to building LMs
    Self-testing understanding and critiquing
    Interpreting the work of others
  2. Mastering abstraction
    Recognising what an LM is (not)
    Identifying relevant concepts and applicable methods/tools
    Mastering the meta-level, modelling LMs
    Associating different relevant concepts with LMs
    Instrumentalising abstract concepts relevant to LMs
  3. Implementing LMs
    Using tools in the LM context
    Exploring the effects of assumptions on a concept
    Programming (and testing) LMs
    Assessing the adequacy and complexity of LM programs

Course Disposition

Foundation is a series of lectures on established literature. Invited lectures focus on deep technical subjects and applications of learning machines. Use of new interactive software platforms for building learning machines.

Literature and preparations

Specific prerequisites *

Ph.D students and master students planning to enroll on a Ph.D program.

Recommended Prerequisites:

Discrete mathematics, linear algebra, machine learning, programming, AI.

Recommended prerequisites

No information inserted


None, lab work can be done on personal computing devices or on accessible servers.


Relevant articles and research papers, plus documentation from Internet sources. During the course, a compendium will be developed, with all of the lecturers (and possibly their collaborators or students) as invited contributors. Students will be motivated to comment on, and influence the contents of, the compendium. Such influence can come in the form of course examination. The outcome will not be a collection of individual chapters by individual authors, but rather a monograph with many co-authors.

Some default lecturers have already suggested literature to cover their respective lectures.

Example (Lee):

  1. Solon Barocas, Sophie Hood, Malte Ziewitz. (2013) Governing Algorithms: A Provocation Piece
  2. Ziewitz, M. (2011). How to think about an algorithm? Notes from a not quite random walk. Discussion paper for Symposium on "Knowledge Machines between Freedom and Control", 29 September 2011.

Examination and completion

If the course is discontinued, students may request to be examined during the following two academic years.

Grading scale *

P, F

Examination *

  • EXA1 - Examination, 7.5 credits, Grading scale: P, F

Based on recommendation from KTH’s coordinator for disabilities, the examiner will decide how to adapt an examination for students with documented disability.

The examiner may apply another examination format when re-examining individual students.

Flexible exam: essay, documented program, contribution to course compendium, documented applied work at company, All types of exam have the same deadline.

Other requirements for final grade *

Completed exam and at least two thirds of the lectures attended.

Opportunity to complete the requirements via supplementary examination

No information inserted

Opportunity to raise an approved grade via renewed examination

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Magnus Boman

Ethical approach *

  • All members of a group are responsible for the group's work.
  • In any assessment, every student shall honestly disclose any help received and sources used.
  • In an oral assessment, every student shall be able to present and answer questions about the entire assignment and solution.

Further information

Course web

Further information about the course can be found on the Course web at the link below. Information on the Course web will later be moved to this site.

Course web FIK3616

Offered by

EECS/Software and Computer Systems

Main field of study *

No information inserted

Education cycle *

Third cycle

Add-on studies

No information inserted

Postgraduate course

Postgraduate courses at EECS/Software and Computer Systems