course-details-portlet

AIS2101 - Intelligent Systems

About

Examination arrangement

Examination arrangement: Portfolio assessment
Grade: Letter grades

Evaluation Weighting Duration Grade deviation Examination aids
Portfolio assessment 100/100

Course content

Selected topics will be announced at the start of the semester and may include some of the following:

  • Introduction to artificial intelligence
  • Rule-based expert systems
  • Frame-based systems
  • Fuzzy logic and fuzzy expert systems
  • Agent-based modelling and simulation
  • Evolutionary algorithms
  • Machine learning
  • Artificial neural networks
  • Reinforcement learning
  • Hybrid intelligent systems
  • Possibly other topics

Learning outcome

Knowledge

  • The candidate can explain and compare theory, principles, applications, strengths and weaknesses of methods presented in the course

Skills

  • The candidate can demonstrate the use of methods presented in the course, both through digital tools and simulation

General competence

  • The candidate can use digital tools for implementation of intelligent systems
  • The candidate can explain the value of intelligent systems for sustainable processes, services, or systems
  • The candidate can present problems and relevant solution methods in a professional and scientific manner
  • The candidate can discuss ethical challenges of artificial intelligence

Learning methods and activities

Learning activities generally include a mix of lectures, tutorials and practical lab/project work. A constructivist approach for learning is endorsed, with focus on problem solving and practical application of theory.

Further on evaluation

The final grade is based on an overall evaluation of the portfolio. The portfolio consists of work that is carried out and documented through digital submissions during the term. The number of assignments is generally consistent with the number of taught topics (typically 4-6). Both individual and team assignments may be given. Assignments are designed to help students achieve specific course learning outcomes, and feedback is given to students during the period of the portfolio. The re-sit exam is an oral exam in August.

Specific conditions

Admission to a programme of study is required:
Automation and Intelligent Systems - Engineering (BIAIS)

Required previous knowledge

The course has no prerequisites. It is a requirement that students are enrolled in the study programme to which the course belongs.

Course materials

An updated course overview, including curriculum, is presented at the start of the semester and will typically also include English material.

Credit reductions

Course code Reduction From To
IE303312 7.5 AUTUMN 2021
More on the course

No

Facts

Version: 1
Credits:  7.5 SP
Study level: Intermediate course, level II

Coursework

Term no.: 1
Teaching semester:  SPRING 2025

Language of instruction: English, Norwegian

Location: Ålesund

Subject area(s)
  • Computer and Information Science
  • Engineering Cybernetics
  • Engineering
Contact information
Course coordinator: Lecturer(s):

Department with academic responsibility
Department of ICT and Natural Sciences

Timetable

List view
Calendar view

Examination

Examination arrangement: Portfolio assessment

Term Status code Evaluation Weighting Examination aids Date Time Examination system Room *
Spring NY Portfolio assessment 100/100 INSPERA
Room Building Number of candidates
Spring ORD Portfolio assessment 100/100 INSPERA
Room Building Number of candidates

Examination arrangement: Oral exam

Term Status code Evaluation Weighting Examination aids Date Time Examination system Room *
Summer UTS Oral exam 100/100
Room Building Number of candidates
  • * The location (room) for a written examination is published 3 days before examination date. If more than one room is listed, you will find your room at Studentweb.
Examination

For more information regarding registration for examination and examination procedures, see "Innsida - Exams"

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