FIK3616 Learning Machines 7.5 credits

• 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)

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

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

Recommended Prerequisites:

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

• 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.

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

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

Magnus Boman

• 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.