Course - Applied AI and Control - IP505245
IP505245 - Applied AI and Control
Lessons are not given in the academic year 2021/2022
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
- ANN-based controller for force allocation in DP operation
- Thruster fault detection and isolation
- Deep reinforcement learning for COLREgs-ompliant maneuvering
- Sea state estimation…
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.
- Individual Mandatory Assignments
Further on evaluation
Final project 60% + oral exam 40%
Recommended previous knowledge
The students are suggested to have the basic knowledge of linear algebra, statistics and some programming experience.
Required previous knowledge
- Jackson, Philip C. Introduction to artificial intelligence. Courier Dover Publications, 2019.
- Bishop, Christopher M. Pattern recognition and machine learning. springer, 2006.
- A Beginner's Guide to Deep Reinforcement Learning, https://pathmind.com/wiki/deep-reinforcement-learning
Credits: 7.5 SP
Study level: Second degree level
Language of instruction: English
- Computer and Information Science
- Computer Science
- Marine Technology
Department with academic responsibility
Department of Ocean Operations and Civil Engineering
- * 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.
For more information regarding registration for examination and examination procedures, see "Innsida - Exams"