Course contents *

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 should be able to

1. apply different principles of Artificial Intelligence (AI)
2. choose appropriate tools and implement efficient solutions to problems in AI
3. integrate tools to design computer programs that show different properties that are expected by an intelligent system
4. present, analyse, and entitle an own solution to an AI problem
5. 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.

Course Disposition

No information inserted

Specific prerequisites *

Completed courses in all of the following fields:

• mathematics equivalent SF1546 Numerical methods course and SF1901 Mathematical Statistics
• Programming equivalent DD1337 programming
• algorithms and data structures equivalent DD1338 Algorithms and Data Structures.

Active participation in a course offering where the final examination is not yet reported in LADOK is considered equivalent to completion of the course. This applies only to students who are first-time registered for the prerequisite course offering or have both that and the applied-for course offering in their individual study plan.

Recommended prerequisites

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. 

Equipment

No information inserted

Literature

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Grading scale *

A, B, C, D, E, FX, F

Examination *

• LAB1 - Labs, 4.0 credits, Grading scale: P, 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

No information inserted

Opportunity to raise an approved grade via renewed examination

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Examiner

Jana Tumová

Iolanda Dos Santos Carvalho Leite

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.

Offered by

EECS/Intelligent Systems

Main field of study *

Computer Science and Engineering

Education cycle *

Second cycle

Add-on studies

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

Contact

Jana Tumova, tumova@kth.se

Transitional regulations *

The earlier course moment TEN1 is replaced by TEN2 and RAP1.

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.

Supplementary information

In this course, the EECS code of honor applies, see:
http://www.kth.se/en/eecs/utbildning/hederskodex