course-details-portlet

MMA4007 - Applied AI and Control

About

New from the academic year 2024/2025

Examination arrangement

Examination arrangement: Aggregate score
Grade: Letter grades

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

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 maritime engineering and applications in the common maritime engineering project of the study program track. 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

Knowledge and skills

  • Understand AI methods, their advantages, and limitations; comprehend the scope and challenges of AI and control in maritime applications.
  • Gain skills in data collection, analysis, and purification.
  • Learn various AI and control methods, including supervised, unsupervised, and reinforcement learning, as well as deep learning techniques.
  • Understand AI applications in maritime engineering, like ship motion prediction, engine fault diagnosis, etc.
  • Contrast classical control systems with data-driven methods for a comprehensive understanding of maritime engineering challenges.

Competence

  • Develop the competence to handle data effectively, formulate problems, simplify model complexity, and select appropriate AI methods for maritime engineering projects.
  • Gain the ability to design and implement AI algorithms for real-world maritime applications.
  • Enhance skills to work and contribute effectively as part of an interdisciplinary team, focusing on applied AI and control in maritime settings.

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 related to the maritime engineering project of the study program track. 75% of the mandatory assignments have to be approved before admission to examination.

Compulsory assignments

  • Mandatory assignment

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:
Mechatronics and Automation (MSMECAUT)
Product and System Engineering (840MD)

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 2025

Language of instruction: English

Location: Ålesund

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

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 *
Spring ORD Assignment 60/100 INSPERA
Room Building Number of candidates
Spring ORD Oral exam 40/100 E
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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