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ID1214 Artificial Intelligence and Applied Methods 7.5 credits

Administer About course

The course gives an overview of Artifical Intelligence and Applied Methods.

The focus is on several different areas of Artifical Intelligence with AI-problems, and Methods and includes areas such as: Intelligent /Knowledge-based systems, Agent / multi-agent systems, Natural language processing and strategies.

Information per course offering

Termin

Information for Autumn 2024 Start 28 Oct 2024 programme students

Course location

KTH Kista

Duration
28 Oct 2024 - 13 Jan 2025
Periods
P2 (7.5 hp)
Pace of study

50%

Application code

50184

Form of study

Normal Daytime

Language of instruction

English

Number of places

Places are not limited

Target group

Open to all programmes as long as it can be included in your programme.

Planned modular schedule
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Contact

Examiner
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Course coordinator
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Teachers
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Contact

Anne Håkansson (annehak@kth.se)

Course syllabus as PDF

Please note: all information from the Course syllabus is available on this page in an accessible format.

Course syllabus ID1214 (Autumn 2024–)
Headings with content from the Course syllabus ID1214 (Autumn 2024–) are denoted with an asterisk ( )

Content and learning outcomes

Course contents

The following fields are treated within the scope of the course:

  • Fundamental AI problems and solutions including search algorithms and planning, knowledge representation forms and knowledge including reasoning strategies, decision support and heuristics.
  • Intelligent agents and multi-agent systems
  • Automatic analysis and generation of natural language.
  • Machine learning and neural networks.

Focus is on artificial intelligence for knowledge-based systems, agent system and strategies.

Intended learning outcomes

After passing the course, the students should be able to:

  • give an account of artificial intelligence and its application areas
  • know and account for artificial intelligence methods and technologies
  • formulate and carry out a well delimited and qualified assignment that applies artificial intelligence techniques.

Literature and preparations

Specific prerequisites

  • Knowledge in Calculus in One Variable, 5 credits, equivalent to completed course IX1303/SF1685/HF1006
  • Knowledge in linear algebra, 5 credits, equivalent to completed course IX1304/SF1684/HF1006
  • Knowledge in Discrete Mathematics, 7,5 credits, equivalent to completed course IX1500/SF1610/CM1000
  • Knowledge in Probability Theory and Statistics, 6 credits, equivalent to completed course IX1501/SF1900/HF1012
  • Knowledge and skills in programming, 6 credits, equivalent to completed course ID1018/HI1024
  • Knowledge in Algorithms and Data Structures, 6 credits, equivalent to completed course ID1021/HI1029
  • Additional skills in independent software development, 12 credits, from completed courses in computer science, computer technology or numerical methods with laboratory elements that are not carried out in groups larger than two people. These courses are in addition to the above mentioned courses.

Active participation in a course offering where the final examination is not yet reported in LADOK is considered equivalent to completion of the course. 

Registering for a course is counted as active participation. 

The term 'final examination' encompasses both the regular examination and the first re-examination.

Equipment

No information inserted

Literature

No information inserted

Examination and completion

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

Grading scale

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

Examination

  • INL1 - Written assignment, 4.0 credits, grading scale: P, F
  • TEN1 - Examination, 3.5 credits, grading scale: A, B, C, D, E, FX, 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.

Written examination. Written assignment that is reported in groups.

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

Profile picture Anne Håkansson

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 room in Canvas

Registered students find further information about the implementation of the course in the course room in Canvas. A link to the course room can be found under the tab Studies in the Personal menu at the start of the course.

Offered by

EECS/Computer Science

Main field of study

Technology

Education cycle

First cycle

Add-on studies

No information inserted

Contact

Anne Håkansson (annehak@kth.se)

Supplementary information

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