Degree Programme in Intelligent Systems
Intelligent Systems 2025, Vasa
Intelligent Systems 2024, Vasa
Enrollment
02.12.2024 - 31.12.2024
Timing
01.01.2025 - 31.03.2025
Number of ECTS credits allocated
5 op
Mode of delivery
Contact teaching
Unit
Faculty of Technology and Seafaring
Campus
Vasa, Wolffskavägen 33
Teaching languages
- English
Degree programmes
- Degree Programme in Intelligent Systems
Teachers
- Jan Berglund
- Joachim Böling
Teacher in charge
Ray Pörn
Groups
-
IS24H-VIntelligent Systems, 2024, part-time studies
Objective
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.
Content
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.
Materials
The study material will be provided by the lecturer.
Evaluation scale
H-5
Assessment criteria, satisfactory (1)
Grade 1: Satisfactory skills in the development of automation systems
Assessment criteria, good (3)
Grade 3: Good skills in the development of automation systems
Assessment criteria, excellent (5)
Grade 5: Excellent skills in the development of automation systems
Qualifications
No prerequisites.
Enrollment
15.06.2024 - 29.09.2024
Timing
22.09.2024 - 31.12.2024
Number of ECTS credits allocated
5 op
Mode of delivery
Contact teaching
Unit
Faculty of Technology and Seafaring
Campus
Vasa, Wolffskavägen 33
Teaching languages
- English
Degree programmes
- Degree Programme in Intelligent Systems
Teachers
- Joachim Böling
- Ray Pörn
Teacher in charge
Ray Pörn
Groups
-
IS24H-VIntelligent Systems, 2024, part-time studies
Objective
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
Content
The contents of the course are concept and principles of dynamic systems, system modeling and simulation, techniques for system identification and optimization.
Materials
The study material will be provided by the lecturer.
Evaluation scale
H-5
Assessment criteria, satisfactory (1)
Grade 1: Satisfactory skills in systems thinking, modeling, identification, and optimization.
Assessment criteria, good (3)
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.
Qualifications
No prerequisites.
Enrollment
01.12.2024 - 31.01.2025
Timing
01.02.2025 - 25.05.2025
Number of ECTS credits allocated
5 op
Mode of delivery
Contact teaching
Unit
Faculty of Technology and Seafaring
Campus
Vasa, Wolffskavägen 33
Teaching languages
- English
Degree programmes
- Degree Programme in Intelligent Systems
Teachers
- Christoffer Björkskog
- Ray Pörn
Teacher in charge
Ray Pörn
Groups
-
IS24H-VIntelligent Systems, 2024, part-time studies
Objective
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.
Content
The course will cover deep learning techniques such as object detection, predictive maintenance, forecasting models, and generative design.
Materials
The study material will be provided by the lecturer.
Evaluation scale
H-5
Assessment criteria, satisfactory (1)
Grade 1: Satisfactory skills in industrial artificial intelligence
Assessment criteria, good (3)
Grade 3: Good skills in industrial artificial intelligence
Assessment criteria, excellent (5)
Grade 5: Excellent skills in industrial artificial intelligence
Qualifications
No prerequisites.
Enrollment
15.06.2024 - 22.09.2024
Timing
25.08.2024 - 31.12.2024
Number of ECTS credits allocated
5 op
Mode of delivery
Contact teaching
Unit
Faculty of Technology and Seafaring
Campus
Vasa, Wolffskavägen 33
Teaching languages
- English
Degree programmes
- Degree Programme in Intelligent Systems
Teachers
- Ray Pörn
- Johan Westö
Teacher in charge
Ray Pörn
Groups
-
IS24H-VIntelligent Systems, 2024, part-time studies
Objective
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
Content
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
Materials
The study material will be provided by the lecturer.
Evaluation scale
H-5
Assessment criteria, satisfactory (1)
Grade 1: Satisfactory skills in understanding and development of intelligent systems.
Assessment criteria, good (3)
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.
Qualifications
No prerequisites.
Enrollment
15.06.2024 - 10.11.2024
Timing
11.11.2024 - 18.02.2025
Number of ECTS credits allocated
5 op
Mode of delivery
Contact teaching
Unit
Faculty of Technology and Seafaring
Campus
Vasa, Wolffskavägen 33
Teaching languages
- English
Degree programmes
- Degree Programme in Intelligent Systems
Teachers
- Ray Pörn
Teacher in charge
Ray Pörn
Groups
-
IS24H-VIntelligent Systems, 2024, part-time studies
Objective
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
Content
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.
Materials
The study material will be provided by the lecturer.
Evaluation scale
H-5
Assessment criteria, satisfactory (1)
Grade 1: Satisfactory skills in understanding and applying ML techniques for classification, prediction, and identification.
Assessment criteria, good (3)
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.
Qualifications
No prerequisites.
Enrollment
02.12.2024 - 31.12.2024
Timing
01.04.2025 - 15.06.2025
Number of ECTS credits allocated
5 op
Mode of delivery
Contact teaching
Unit
Faculty of Technology and Seafaring
Campus
Vasa, Wolffskavägen 33
Teaching languages
- English
Degree programmes
- Degree Programme in Intelligent Systems
Teachers
- Joachim Böling
- Ray Pörn
Teacher in charge
Ray Pörn
Groups
-
IS24H-VIntelligent Systems, 2024, part-time studies
Objective
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
Content
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.
Materials
The study material will be provided by the lecturer.
Evaluation scale
H-5
Assessment criteria, satisfactory (1)
Grade 1: Satisfactory skills in the project work
Assessment criteria, good (3)
Grade 3: Good skills in the project work
Assessment criteria, excellent (5)
Grade 5: Excellent skills in the project work
Qualifications
No prerequisites.