The course gives a broad overview of the problems and methods studied in the field of artificial intelligence.
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Content and learning outcomes
The following fields are treated within the scope of the course: problem-solving with search algorithms, heuristics, knowledge representations (logic), planning, representation of uncertainty and inference (Bayesian networks, HMM), decision theory and utility theory, diction (NLP).
Intended learning outcomes
After passing the course, the student shall be able to
- apply different principles of Artificial Intelligence (AI)
- choose appropriate tools and implement efficient solutions to problems in AI
- integrate tools to design computer programs that show different properties that are expected by an intelligent system
- present, analyse, and entitle an own solution to an AI problem
- reflect on and discuss current social and ethical aspects of AI
in order to be able to
- draw use of methods of artificial intelligence in analysis, design and implementation of computer programs
- contribute to design of an intelligent system in both academic and industrial applications.
Literature and preparations
- Knowledge in algebra and geometry, 7.5 higher education credits, equivalent to completed course SF1624.
- Knowledge in one variable calculus, 7.5 higher education credits, equivalent to completed course SF1625.
- Knowledge and skills in programming, 6 higher education credits, equivalent to completed course DD1337/DD1310/DD1311/DD1312/DD1314/DD1315/DD1316/DD1318/DD1321/DD1331/DD100N/ID1018.
- Knowledge in algorithms and data structures, at least 6 higher education credits, equivalent to completed course DD1320/DD1321/DD1325/DD1327/DD1338/DD2325/ID1020/ID1021.
Active participation in a course offering where the final examination is not yet reported in LADOK is considered equivalent to completion of the course.
Being registered for a course counts as active participation.
The term 'final examination' encompasses both the regular examination and the first re-examination.
Students who are planning to take the course autumn semester 2020 are given exemption from the specific prerequisite regarding regarding mathematics corresponding to SF1546 Numerical methods basic course.
Examination and completion
If the course is discontinued, students may request to be examined during the following two academic years.
- LAB2 - Laboratory work, 4.0 credits, grading scale: A, B, C, D, E, FX, F
- RAP1 - Report, 0.5 credits, grading scale: P, F
- TEN2 - Written exam, 1.5 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.
Opportunity to complete the requirements via supplementary examination
Opportunity to raise an approved grade via renewed examination
- 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 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 DD2380
Main field of study
DD2431 Machine Learning
DD2434 Machine Learning, advanced course
DD2424 Deep learning in data science
DD2432 Artificial Neural Networks and Other Learning Systems
DD2423 Image Analysis and Computer Vision
DD2425 Robotics and Autonomous Systems
DD2429 Computational Photography
EL2320 Applied Estimation
The earlier examination LAB1 has been replaced by LAB2.
In this course, the EECS code of honor applies, see: