course-details-portlet

IP505245 - Applied AI and Control

About

Examination arrangement

Examination arrangement: Aggregate score
Grade: Letter grades

Evaluation Weighting Duration Grade deviation Examination aids
Assignment 60/100
Oral examination 40/100 30 minutes

Course content

The course is open for the students who are interested in artificial intelligence (AI) and willing to apply AI to practical applications. Focus will be on principles and implementation of AI methods for marine engineering. Throughout the course, students will gain the knowledge of concept, methodology and experiments from examples of real projects in marine domain. The course content are as follows:

  • AI introduction
  • Data collection, analysis and purification
  • AI and control methods
    • supervised learning
    • unsupervised learning
    • reinforcement learning
    • deep learning…
  • AI in different applications
    • Ship motion prediction
    • Engine fault diagnosis and prognosis
    • ANN-based controller for ship docking
    • Thruster fault detection and isolation
    • Deep reinforcement learning for COLREgs-compliant maneuvering
    • Sea state estimation…

Learning outcome

The students are expected to:

  • Have a good understanding of AI methods and their pros and cons;
  • Have knowledge of challenges in marine applications;
  • Know how to deal with data, formulate the problem, simplify model complicity, and select AI methods;
  • Be able to design and implement their own AI algorithms for real applications.

Learning methods and activities

Lectures, exercises and examples from real applications will be provided in the course. There will be individual mandatory assignments and exam project. 75% of the mandatory assignments have to be approved before admission to examination.

Compulsory assignments

  • Individual Mandatory Assignments

Further on evaluation

Final project 60% + oral exam 40%.

Resit exam can be carried out for the individual partial assessment and is offered the following semester.

You are given the opportunity to complain about partial assessments in this subject before all partial assessments have been completed.

Specific conditions

Admission to a programme of study is required:
Naval Architecture (850MD)
Naval Architecture (850ME)
Product and System Engineering (840MD)
Product and Systems Design (845ME)

Required previous knowledge

None.

Course materials

  • Jackson, Philip C. Introduction to artificial intelligence. Courier Dover Publications, 2019.
  • Bishop, Christopher M. Pattern recognition and machine learning. springer, 2006.
  • Sutton, Richard S., Barto, Andrew G. Reinforcement learning: An introduction. MIT press, 2018.
  • A Beginner's Guide to Deep Reinforcement Learning, https://pathmind.com/wiki/deep-reinforcement-learning

More on the course

No

Facts

Version: 1
Credits:  7.5 SP
Study level: Second degree level

Coursework

Term no.: 1
Teaching semester:  SPRING 2024

Language of instruction: English

Location: Ålesund

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

Department with academic responsibility
Department of Ocean Operations and Civil Engineering

Timetable

List view
Calendar view

Examination

Examination arrangement: Aggregate score

Term Status code Evaluation Weighting Examination aids Date Time Examination system Room *
Autumn UTS Assignment 60/100 INSPERA
Room Building Number of candidates
Autumn UTS Oral examination 40/100
Room Building Number of candidates
Spring ORD Assignment 60/100

Submission
2024-05-23


12:00

INSPERA
Room Building Number of candidates
Spring ORD Oral examination 40/100 2024-05-30
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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