Program Overview

ID1214 Artificial Intelligence and Applied Methods

The course gives an overview of Artificial Intelligence and Applied Methods, focusing on several different areas of Artificial Intelligence with AI-problems, and Methods, including areas such as:

• Intelligent/Knowledge-based systems
• Agent/multi-agent systems
• Natural language processing and strategies

Information per Course Offering

Choose semester and course offering to see current information and more about the course, such as course syllabus, study period, and application information.

Autumn 2026 Start

• Course location: KTH Campus
• Duration: 26 Oct 2026 - 11 Jan 2027
• Periods: Autumn 2026: P2 (7.5 hp)
• Pace of study: 50%
• Application code: 10751
• Form of study: Normal Daytime
• Language of instruction: English
• Number of places: Min: 1
• Target group: Open to all programmes as long as it can be included in your programme
• Part of programme:
  • Degree Programme in Computer Engineering, year 3, DPU2, Mandatory
  • Degree Programme in Computer Engineering, year 3, SAIN
  • Degree Programme in Information and Communication Technology, year 2
  • Degree Programme in Information and Communication Technology, year 3
  • Bachelor's Programme in Information and Communication Technology, year 3

Course Syllabus

The course syllabus is available in an accessible format on this page.

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

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

Examination and Completion

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

Examiner

Anne Hňkansson

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

Offered by

EECS/Computer Science

Main Field of Study

Technology

Education Cycle

First cycle

Supplementary Information

In this course, the EECS code of honor applies.