Artificial Intelligence and Machine LearningLaajuus (5 cr)
Code: IME22LE08
Credits
5 op
Objective
The students are introduced to artificial intelligence (AI) and machine learning (MI) concepts and applications in the field of Industrial Management and Engineering. The students will gain insights into the analysis process that starts with problem identification and data collection, continues with choice of appropriate tools and ends with an evaluation of the results. During the course the student is given the opportunity to apply the lessons learnt to a problem from their own day-to-day work environment.
Content
The course seeks to answer the following questions:
- What is AI and ML?
- Where and how are AI and ML learning used today?
- What added value can AI and ML bring to Industrial
Management and Engineering?
- How are different models created for AI and ML?
- What methods and tools are used within AI and ML?
- How are the results of AI and ML evaluated?
Qualifications
See Study Handbook 12-2018, page 17
Materials
- Steven Finlay 2017. Artificial Intelligence and Machine Learning for Business.
- Ajay Agrawal, Joshua Gans and Avi Goldfarb 2018. Prediction Machines.
- Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani 2017. An Introduction to Statistical Learning: with Applications in R.
- Journal articles suggested by the examiner.
- Meriluoto, Antti 2018. Tekoäly, matkaopas johtajalle.
- Tegmark, Max 2017. Att vara människa i den artificiella
intelligensens tid.
Enrollment
02.07.2025 - 31.07.2025
Timing
01.08.2025 - 26.10.2025
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
Teachers
- Mats Braskén
- Ray Pörn
Teacher in charge
Ray Pörn
Objective
The students are introduced to artificial intelligence (AI) and machine learning (MI) concepts and applications in the field of Industrial Management and Engineering. The students will gain insights into the analysis process that starts with problem identification and data collection, continues with choice of appropriate tools and ends with an evaluation of the results. During the course the student is given the opportunity to apply the lessons learnt to a problem from their own day-to-day work environment.
Content
The course seeks to answer the following questions:
- What is AI and ML?
- Where and how are AI and ML learning used today?
- What added value can AI and ML bring to Industrial
Management and Engineering?
- How are different models created for AI and ML?
- What methods and tools are used within AI and ML?
- How are the results of AI and ML evaluated?
Materials
- Steven Finlay 2017. Artificial Intelligence and Machine Learning for Business.
- Ajay Agrawal, Joshua Gans and Avi Goldfarb 2018. Prediction Machines.
- Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani 2017. An Introduction to Statistical Learning: with Applications in R.
- Journal articles suggested by the examiner.
- Meriluoto, Antti 2018. Tekoäly, matkaopas johtajalle.
- Tegmark, Max 2017. Att vara människa i den artificiella
intelligensens tid.
Evaluation scale
H-5
Qualifications
See Study Handbook 12-2018, page 17
Enrollment
02.07.2024 - 31.07.2024
Timing
01.08.2024 - 30.11.2024
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
Teachers
- Mats Braskén
- Ray Pörn
Groups
-
IME23HP-VIndustrial Management and Engineering, 2023, part-time studies
Objective
The students are introduced to artificial intelligence (AI) and machine learning (MI) concepts and applications in the field of Industrial Management and Engineering. The students will gain insights into the analysis process that starts with problem identification and data collection, continues with choice of appropriate tools and ends with an evaluation of the results. During the course the student is given the opportunity to apply the lessons learnt to a problem from their own day-to-day work environment.
Content
The course seeks to answer the following questions:
- What is AI and ML?
- Where and how are AI and ML learning used today?
- What added value can AI and ML bring to Industrial
Management and Engineering?
- How are different models created for AI and ML?
- What methods and tools are used within AI and ML?
- How are the results of AI and ML evaluated?
Materials
- Steven Finlay 2017. Artificial Intelligence and Machine Learning for Business.
- Ajay Agrawal, Joshua Gans and Avi Goldfarb 2018. Prediction Machines.
- Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani 2017. An Introduction to Statistical Learning: with Applications in R.
- Journal articles suggested by the examiner.
- Meriluoto, Antti 2018. Tekoäly, matkaopas johtajalle.
- Tegmark, Max 2017. Att vara människa i den artificiella
intelligensens tid.
Evaluation scale
H-5
Qualifications
See Study Handbook 12-2018, page 17
Enrollment
15.06.2023 - 27.08.2023
Timing
28.08.2023 - 18.11.2023
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
Teachers
- Mats Braskén
- Ray Pörn
Groups
-
IME22HP-VIndustrial Management and Engineering, 2022, part-time studies
Objective
The students are introduced to artificial intelligence (AI) and machine learning (MI) concepts and applications in the field of Industrial Management and Engineering. The students will gain insights into the analysis process that starts with problem identification and data collection, continues with choice of appropriate tools and ends with an evaluation of the results. During the course the student is given the opportunity to apply the lessons learnt to a problem from their own day-to-day work environment.
Content
The course seeks to answer the following questions:
- What is AI and ML?
- Where and how are AI and ML learning used today?
- What added value can AI and ML bring to Industrial
Management and Engineering?
- How are different models created for AI and ML?
- What methods and tools are used within AI and ML?
- How are the results of AI and ML evaluated?
Evaluation scale
H-5
Qualifications
See Study Handbook 12-2018, page 17