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 *
Autonomously solving problems Applying existing as well as future tools to building LMs Self-testing understanding and critiquing Interpreting the work of others
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
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
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.
Discrete mathematics, linear algebra, machine learning, programming, AI.
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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.
Solon Barocas, Sophie Hood, Malte Ziewitz. (2013) Governing Algorithms: A Provocation Piece http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2245322
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 *
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
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Opportunity to raise an approved grade via renewed examination