Academic year 2024 - 2025

•   Development of intelligent automation systems INS24IS04-3001 01.01.2025-31.03.2025  5 credits  (IS24H-V) +-
  Competence objectives of the study unit

  The development of intelligent systems for process automation and controls involves understanding a chain of events. Proper understanding of research plans, specifications, project work and management are fundamental elements for the development of automation systems. The student will learn how to set up requirements and structures for development projects. The student will also understand the importance of life cycle planning and the effects of obsolescence. Supervision, data acquisition, controls, communication, and networks are subjects that will be studied in the course. The student will learn the process and steps needed for the successful development of an intelligent automation system.
  
  Knowledge and understanding
  After completing the course, the student should be able to:
  - identify and discuss requirements needed for automation system development projects
  - describe how life cycle and obsolescence management can be applied to automation systems
  - recognize the different main areas for a modern automation system.
  
  Skills and abilities
  After completing the course, the student should be able to:
  - conduct requirements collection for automation system development projects
  - apply obsolescence management principles and common practices
  - analyze the needs and required elements for developing a modern automation system.
  
  Evaluation ability and approach
  After completing the course, the student should be able to:
  - plan the development for a modern automation system
  - assess and report on a system’s life cycle state in terms of obsolescence
  - propose solutions for lifetime extension and modernization of automation systems.

  Prerequisites

  No prerequisites.

  Content of the study unit

  The course content focuses on needs and requirements for developing intelligent automation systems that are capable of hosting modern elements. Aspects related to sustainability and life cycle management for automation systems are studied. Concepts and methods for managing requirements and obsolescence are central parts.

  Assessment criteria

  Assessment criteria – satisfactory (1-2)

  Grade 1: Satisfactory skills in the development of automation systems

  Assessment criteria – good (3-4)

  Grade 3: Good skills in the development of automation systems

  Assessment criteria – excellent (5)

  Grade 5: Excellent skills in the development of automation systems

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  Person(s) in charge

  Ray Pörn

  Mode of delivery

  Face-to-face

  Learning material

  The study material will be provided by the lecturer.

  Teaching language

  English

  Timing

  01.01.2025 - 31.03.2025

  Enrollment date range

  02.12.2024 - 31.12.2024

  • 5 credits

  Group(s)

  • IS24H-V

  Teachers and responsibilities

  Jan Berglund, Joachim Böling

  Responsible unit

  Faculty of Technology and Seafaring

  Degree Programme(s)

  Degree Programme in Intelligent Systems

  Campus

  Vasa, Wolffskavägen 33

  Assessment scale

  H-5

•   Dynamical systems INS24IS02-3001 22.09.2024-15.12.2024  5 credits  (IS24H-V) +-
  Competence objectives of the study unit

  Understanding of process systems is a central part of industrial automation. Dynamical systems are described through modeling and identification. The course aims at introducing different modeling techniques to the students to achieve a common platform of knowledge. The student will learn how to recognize, analyze, and describe different dynamic systems and to construct models, both model- and data driven ones, of systems and assess their quality and performance.
  
  Knowledge and understanding
  After completing the course, the student should be able to:
  - recognize and describe common dynamic process systems
  - explain the difference between various systems
  - describe the practical usage of some process models
  
  Skills and abilities
  After completing the course, the student should be able to:
  - create and simulate process models
  - experiment with different process models
  - perform basic identification of processes
  
  Evaluation ability and approach
  After completing the course, the student should be able to:
  - justify the choice of a process model
  - evaluate and report on the choice of a model

  Prerequisites

  No prerequisites.

  Content of the study unit

  The contents of the course are concept and principles of dynamic systems, system modeling and simulation, techniques for system identification and optimization.

  Assessment criteria

  Assessment criteria – satisfactory (1-2)

  Grade 1: Satisfactory skills in systems thinking, modeling, identification, and optimization.

  Assessment criteria – good (3-4)

  Grade 3: Good skills in systems thinking, modeling, identification, and optimization.

  Assessment criteria – excellent (5)

  Grade 5: Excellent skills in systems thinking, modeling identification, and optimization.

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  Person(s) in charge

  Ray Pörn

  Mode of delivery

  Face-to-face

  Learning material

  The study material will be provided by the lecturer.

  Teaching language

  English

  Timing

  22.09.2024 - 15.12.2024

  Enrollment date range

  15.06.2024 - 29.09.2024

  • 5 credits

  Group(s)

  • IS24H-V

  Teachers and responsibilities

  Joachim Böling, Ray Pörn

  Responsible unit

  Faculty of Technology and Seafaring

  Degree Programme(s)

  Degree Programme in Intelligent Systems

  Campus

  Vasa, Wolffskavägen 33

  Assessment scale

  H-5

•   Industrial artificial intelligence INS24IS05-3001 01.02.2025-30.04.2025  5 credits  (IS24H-V) +-
  Competence objectives of the study unit

  The course will cover different AI-based approaches for industrial automation and autonomy. The student will get knowledge about deep learning techniques, such as object detection, image classification and segmentation and their use, for example, in quality control and supervision. The student will learn the working principles of forecasting techniques and generative methods. The student will also learn how to collect data and to train, validate and test model performance. The student will understand the importance of data quality and have insights into different pre-processing methods.
  
  Knowledge and understanding
  After completing the course, the student should be able to:
  - understand how to use deep learning techniques to solve industrial problems.
  - suggest a potential solution approach for a specific problem.
  
  Skills and abilities
  After completing the course, the student should be able to:
  - implement a deep learning solution for practical problems.
  - apply and experiment with different deep learning techniques.
  
  Evaluation ability and approach
  After completing the course, the student should be able to:
  - discuss the opportunities and potential to apply deep learning techniques to solve industrial problems.
  - evaluate and report on the results of the performance of a deep learning model.

  Prerequisites

  No prerequisites.

  Content of the study unit

  The course will cover deep learning techniques such as object detection, predictive maintenance, forecasting models, and generative design.

  Assessment criteria

  Assessment criteria – satisfactory (1-2)

  Grade 1: Satisfactory skills in industrial artificial intelligence

  Assessment criteria – good (3-4)

  Grade 3: Good skills in industrial artificial intelligence

  Assessment criteria – excellent (5)

  Grade 5: Excellent skills in industrial artificial intelligence

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  Person(s) in charge

  Ray Pörn

  Mode of delivery

  Face-to-face

  Learning material

  The study material will be provided by the lecturer.

  Teaching language

  English

  Timing

  01.02.2025 - 30.04.2025

  Enrollment date range

  01.12.2024 - 31.01.2025

  • 5 credits

  Group(s)

  • IS24H-V

  Teachers and responsibilities

  Ray Pörn

  Responsible unit

  Faculty of Technology and Seafaring

  Degree Programme(s)

  Degree Programme in Intelligent Systems

  Campus

  Vasa, Wolffskavägen 33

  Assessment scale

  H-5

•   Introduction to intelligent systems INS24IS01-3001 25.08.2024-30.11.2024  5 credits  (IS24H-V) +-
  Competence objectives of the study unit

  The students will be introduced to intelligent systems in the industrial context. The student will learn what defines an intelligent system and the basic principles of intelligent systems. The student will gain knowledge of the parts that constitute an intelligent system and how they interact.
  
  After completing the course, the student should be able to:
  Knowledge and understanding
  - explain the basic parts and functionality of an intelligent system
  - describe the challenges, risks, and opportunities with intelligent systems
  - understand the working principles of intelligent systems
  
  Skills and abilities
  - analyze the needs and requirements for an intelligent system
  - practice with different methods for realizing intelligent systems
  
  Evaluation ability and approach
  - plan the development of an intelligent system
  - propose and evaluate solutions for intelligent systems

  Prerequisites

  No prerequisites.

  Content of the study unit

  Content
  The course gives an overview of intelligent systems in the industrial automation context. The course serves as a common platform of knowledge for the studies in the program. Content:
  • Fundamentals of industrial automation systems
  • Data-driven and model-based design
  • Automated and autonomous decision making
  • Digital twins
  • Sensors and IoT devices
  • Data collection, storage, and flow
  • Cyber security

  Assessment criteria

  Assessment criteria – satisfactory (1-2)

  Grade 1: Satisfactory skills in understanding and development of intelligent systems.

  Assessment criteria – good (3-4)

  Grade 3: Good skills in understanding and development of intelligent systems.

  Assessment criteria – excellent (5)

  Grade 5: Excellent skills in understanding and development of intelligent systems.

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  Person(s) in charge

  Ray Pörn

  Mode of delivery

  Face-to-face

  Learning material

  The study material will be provided by the lecturer.

  Teaching language

  English

  Timing

  25.08.2024 - 30.11.2024

  Enrollment date range

  15.06.2024 - 22.09.2024

  • 5 credits

  Group(s)

  • IS24H-V

  Teachers and responsibilities

  Ray Pörn, Johan Westö

  Responsible unit

  Faculty of Technology and Seafaring

  Degree Programme(s)

  Degree Programme in Intelligent Systems

  Campus

  Vasa, Wolffskavägen 33

  Assessment scale

  H-5

•   Machine learning methods INS24IS03-3001 11.11.2024-30.01.2025  5 credits  (IS24H-V) +-
  Competence objectives of the study unit

  The course focuses on different machine learning and artificial intelligence-based techniques that can be used for system identification, process optimization, classification, and predictive purposes.
  
  After completing the course, the student:
  - has knowledge of what AI and ML is, where it is used and how it has development
  - can identify and recognize industrial problems that are solvable through machine learning
  - can plan and apply the machine learning workflow for classification and regression problems
  - understands and can explain the difference between different machine learning tasks and techniques
  
  - can recognize different inputs used in AI and machine learning
  - is able to develop and build machine learning models for different tasks
  - can validate and test the performance of a model
  
  - knows how to interpret the result of a machine learning experiment
  - report on the result of a machine learning experiment
  - can assess the outcome of a machine learning experiment

  Prerequisites

  No prerequisites.

  Content of the study unit

  The availability of big amounts of data from various systems makes it possible to apply both linear and nonlinear models. The course focuses on different techniques that can be used for identification, classification, and regression in industrial automation. The course also gives knowledge of feature selection and pre-processing techniques.

  Assessment criteria

  Assessment criteria – satisfactory (1-2)

  Grade 1: Satisfactory skills in understanding and applying ML techniques for classification, prediction, and identification.

  Assessment criteria – good (3-4)

  Grade 3: Good skills in understanding and using ML techniques for classification, prediction, and identification.

  Assessment criteria – excellent (5)

  Grade 5: Excellent skills in understanding and using ML techniques for classification, prediction, and identification. 

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  Person(s) in charge

  Ray Pörn

  Mode of delivery

  Face-to-face

  Learning material

  The study material will be provided by the lecturer.

  Teaching language

  English

  Timing

  11.11.2024 - 30.01.2025

  Enrollment date range

  15.06.2024 - 10.11.2024

  • 5 credits

  Group(s)

  • IS24H-V

  Teachers and responsibilities

  Ray Pörn

  Responsible unit

  Faculty of Technology and Seafaring

  Degree Programme(s)

  Degree Programme in Intelligent Systems

  Campus

  Vasa, Wolffskavägen 33

  Assessment scale

  H-5

•   Machine sensing project INS24IS06-3001 01.04.2025-15.06.2025  5 credits  (IS24H-V) +-
  Competence objectives of the study unit

  To participate in and complete a project in some area of the industrial automation process. The student group will learn to identify and describe the problem/task, and to plan, propose and implement a solution approach, as well as evaluate and assess the result and report on the findings.
  
  Knowledge and understanding
  After completing the course, the student should be able to:
  - identify and explain the problem/task and a possible solution approach
  - discuss and compare different solution approaches
  
  Skills and abilities
  After completing the course, the student should be able to:
  - propose and plan possible solution approaches
  - create an intelligent system for automation
  - apply and experiment with different techniques
  
  Evaluation ability and approach
  After completing the course, the student should be able to:
  - report on and present the findings of the project
  - evaluate and assess the project result
  - propose possible improvements

  Prerequisites

  No prerequisites.

  Content of the study unit

  The explicit theme of the course can vary from year to year. Examples of possible projects:
  • use machine sound for anomaly detection and predictive purposes.
  • apply machine vision for intelligent decision making.
  • project related to intelligent forecasting.

  Assessment criteria

  Assessment criteria – satisfactory (1-2)

  Grade 1: Satisfactory skills in the project work

  Assessment criteria – good (3-4)

  Grade 3: Good skills in the project work

  Assessment criteria – excellent (5)

  Grade 5: Excellent skills in the project work

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  Person(s) in charge

  Ray Pörn

  Mode of delivery

  Face-to-face

  Learning material

  The study material will be provided by the lecturer.

  Teaching language

  English

  Timing

  01.04.2025 - 15.06.2025

  Enrollment date range

  02.12.2024 - 31.12.2024

  • 5 credits

  Group(s)

  • IS24H-V

  Teachers and responsibilities

  Joachim Böling, Ray Pörn

  Responsible unit

  Faculty of Technology and Seafaring

  Degree Programme(s)

  Degree Programme in Intelligent Systems

  Campus

  Vasa, Wolffskavägen 33

  Assessment scale

  H-5

Academic year 2025 - 2026

There is no implementations attached to this degree programme.