autumn 2022
DTE-2501 AI Methods and Applications - 10 ECTS

Last changed 30.09.2022

Type of course

This course can be taken as a single subject course

Course content

The course targets machine-based problem solving and basic Natural Language processing methods. The course introduces swarm intelligence, ant colony optimization, and genetic algorithms. Further on, fuzzy systems and fuzzy computing are introduced along with ensemble techniques, inductive systems, entropy, elementary clustering techniques K-NN, K-means and reinforcement learning along with Dynamic Programming.

Recommended prerequisites

DTE-2602 Introduction to Machine Learning and AI

Objectives of the course

On completion of the course, the successful student is expected to have the following:

Knowledge

The student will have:

  • An overview of numerous approaches in artificial intelligence.
  • Basic understanding of possibilities and limitations of the approaches presented in the course.
  • Basic understanding of the theory and application of the approaches presented in the course.

Skills

The student should be able to:

  • Program, adapt and apply AI algorithms on predefined problems.
  • Analyze and evaluate results of AI algorithms.

General Competence

  • Can apply the knowledge and skills to solve problems and communicate about the results with other specialists in the field of computer science.

Language of instruction and examination

English

Teaching methods

The subject uses so-called "Flipped classroom", i.e., lectures are posted online continuously during the semester in the form of short instructional videos and demonstrations of 10-20 minutes. In addition, exercises and control questions related to each video are used.

The subject teaches in the autumn semester with teacher-led and assistant-led learning and / or exercises. Online students will have access to a teaching assistant for afternoon / evening support.


Schedule

Examination

Examination systems: Date: Grade scale:
Portfolio Innlevering: 09.12.2022 kl. 14:00 A–E, fail F
Coursework requirements – To take an examination, the student must have passed the following coursework requirements:
Mandatory exercises Approved/ Not approved

More info about the coursework requirements

There are 6 mandatory exercises. These exercises can be submitted in either English or Norwegian.  

Exercises submitted after the submission deadline will not be graded.

At least 4 out of the 6 exercises must be passed to qualify for a grade in the course.


More info about the portfolio

Portfolio assessment with the following three components (assessment basis):

  • Two programming works (can be submitted in either English or Norwegian)
  • One e-test for selected parts of the syllabus

The grade will be based on all three components in the portfolio. The weights of the components will be presented in the introductory presentation.


Re-sit examination

If any programming works is missing, the candidate will not qualify for a re-sit examination and must take the course again at the next ordinary period.

The re-sit examination consists of taking a new e-test that replaces the original e-test.


  • About the course
  • Campus: Narvik | Bodø | Annet | Online |
  • ECTS: 10
  • Course code: DTE-2501
  • Tidligere år og semester for dette emnet