Syllabus

Artificial Intelligence

Code
GIK2FB
Points
7.5 Credits
Level
First Cycle
School
School of Information and Engineering
Subject field
Information Systems (IKA)
Group of Subjects
Informatics/Computer and Systems Sciences
Disciplinary Domain
Technology, 100%
This course can be included in the following main field(s) of study
Information Systems1
Microdata Analysis2
Progression indicator within (each) main field of study
1G1F
2G1F
Approved
Approved, 05 March 2020.
This syllabus is valid from 05 March 2020.
Download as pdf To course page

Learning Outcomes

Knowledge and understanding
Upon completion of the course student will be able to:

  • Describe concepts often coupled with artificial intelligence (AI) such as machine learning, search, heuristics, games, intelligence, agents, uncertainty, rationality, knowledge representation, no-free-lunch theorem, reasoning and planning
  • Explain how systems can provide capabilities for reasoning under uncertainty
  • Explain how supervised training and testing works, and how robustness, noise and data selection affect the ability to generalise
  • Explain how presented AI algorithms work conceptually
  • Compare the advantages and disadvantages of some basic AI algorithms
  • Recall the historical development of AI and the current situation

Skills and abilities
Upon completion of the course student will be able to:

  • Design, develop, implement and demonstrate AI algorithms using a programming language
  • Evaluate quantitatively the performance of machine learning and search
  • Select appropriate methods to address problems in the domain of AI
  • Detect “overfitting” and suggest possible solutions to this problem

Values and attitudes
Upon completion of the course student will be able to:

  • Discuss the effect on society of emerging technologies in AI and vice versa

Course Content

This course presents the broad area of artificial intelligence (AI). AI in the context of this course refers to the tools, techniques and methodologies that are used to automate a process.
The course covers the historical development of AI, the foundations of AI, including sustainability and ethical aspects, logic and automated reasoning, the concept of agents, knowledge representation and reasoning, machine learning and search, including evolutionary computing. 

The student receives trained in how to use selected AI algorithms and how to see their possibilities and limitations.

Assessment

Hand-in assignments (2.5 credits), laboratory work (2.5 credits), and written exam (2.5 credits).

Forms of Study

Lectures, workshops, laboratory work, hand-in assignments

Grades

The Swedish grades U–VG.

Laboratory work U-G.

In order to obtain a VG (pass with distinction) as a final grade for the whole course, students require VG in both the individual written exam and hand in assignments. In addition, a G (pass) in the laboratory work is required.

Prerequisites

  • Object-Oriented Programming 7.5 Credits, First cycle or other course in Fundamentals of Programming