Academic year 2020 - 2021

•   Artificial Intelligence, Machine Learning, Human - Machine Interaction AMO18AI01-3004 11.02.2021-31.03.2021  5 credits  (AMO20H-Å) +-
  Competence objectives of the study unit

  The Student
  
  - has an basic understanding of AI and the history of AI.
  - has knowledge of where AI is used and its development today.
  - has an basic understanding of different machine learning algorithms and their future possibilities
  - can recognize different inputs used in AI and machine learning
  - recognises the possibilities to get information and data from different systems and how Human - Machine Interaction is adapted in autonomous vessels

  Prerequisites

  No prerequisites.

  Content of the study unit

  - Administrative matters
  - Computational thinking and algoritms - What is computing? Algorithms and complexity
  - Introduction to AI and Autonomy - What is AI? How do you define Autonomy?
  - Agents and Search - How to solve problems with “Good old-fashioned AI”
  - Introduction to Machine Learning - Overview of ML, Risks and Problems with ML
  - Supervised learning - Basic supervised learning through regression
  - Machine vision - Deep neural networks, Image segmentation, Image detection, Image recognition
  - Reinforcement learning - Reinforcement learning as search, Autonomy and reinforcement learning
  - Industrial Internet - What is IoT? What is a Digital Twin?
  - Sensors and Sensor fusion - Situational awareness, LIDAR, IR, GNSS and IMU’s
  - Autonomy and Safety - Software safety, Liability, Accountability

  Assessment criteria

  Assessment criteria – satisfactory (1-2)

  Sufficient 1
  Theory and methodology are poorly understood and implemented in Autonomous Maritime Operation related tasks/ assignments.
  Research, communication and documentation are hardly acceptable.
  Active participation.
  Satisfactory 2
  Appear to grasp theory and have made a start in showing its applicability in Autonomous Maritime Operation related tasks/ assignments. Research, communication and documentation are acceptable.
  Active participation.

  Assessment criteria – good (3-4)

  Good 3
  Understanding of theory and applicability of methods in Autonomous Maritime Operation related tasks/ assignments, but work could be stronger.
  Research, service design, communication and documentation are good.
  Active participation.
  Very Good 4
  General understanding of theory and methods, very good implementation in Autonomous Maritime Operation related tasks/ assignments.
  Reliable research, innovative service design and communication and documentation on good level.
  Very active participation.

  Assessment criteria – excellent (5)

  Excellent 5
  Mastery of theory and methods, proficiency of implementation of them in Autonomous Maritime Operation related tasks/ assignments.
  Outstanding research, innovative service design and excellent communication and documentation.
  Very active participation.

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  Person(s) in charge

  Thomas Finne

  Mode of delivery

  Face-to-face

  Learning material

  Materials and links on VLE
  Selected parts from Brunton & Kutz "Data Driven Science & Engineering.Machine Learning, Dynamical Systems, and Control", 2019.
  Selected parts from Howard & Gugger "Deep Learning for Coders with fastai and PyTorch: AI Applications Without a PhD", 2020.

  Learning methods

  Lectures and exercises in the classroom and project based learning.

  Objects, timing and methods of assessment

  Based on projects and presentations

  Teaching language

  English

  Timing

  11.02.2021 - 31.03.2021

  Enrollment date range

  01.08.2020 - 10.02.2021

  • 5 credits

  Group(s)

  • AMO20H-Å

  Seats

  1 - 30

  Teachers and responsibilities

  Kim Roos, Thomas Finne

  Responsible unit

  ÅboRaseborg (not translated)

  Degree Programme(s)

  Degree Programme in Autonomous Maritime Operations

  Campus

  Åbo, Hertig Johans parkgata 21

  Assessment scale

  H-5

  Student's schedule and workload

  135 h

  Content scheduling

  11-12.2.2021 Theory and exercises (Aboa Mare)
  13.2.-16.3.2021 Self studies and project studies
  17-18.3.2021 Presentation of results from projects (Aboa Mare)

  Assessment criteria

  Assessment criteria – satisfactory (1-2)

  Sufficient 1
  Theory and methodology are poorly understood and implemented in Autonomous Maritime Operation related tasks/ assignments.
  Research, communication and documentation are hardly acceptable.
  Active participation.
  Satisfactory 2
  Appear to grasp theory and have made a start in showing its applicability in Autonomous Maritime Operation related tasks/ assignments. Research, communication and documentation are acceptable.
  Active participation.

  Assessment criteria – good (3-4)

  Good 3
  Understanding of theory and applicability of methods in Autonomous Maritime Operation related tasks/ assignments, but work could be stronger.
  Research, service design, communication and documentation are good.
  Active participation.
  Very Good 4
  General understanding of theory and methods, very good implementation in Autonomous Maritime Operation related tasks/ assignments.
  Reliable research, innovative service design and communication and documentation on good level.

  Assessment criteria – excellent (5)

  Excellent 5
  Mastery of theory and methods, proficiency of implementation of them in Autonomous Maritime Operation related tasks/ assignments.
  Outstanding research, innovative service design and excellent communication and documentation.
  Very active participation.

Academic year 2021 - 2022

There is no implementations attached to this degree programme.