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Intelligent Automation (4 cr)

Code: DT10041-3002

General information


Enrollment
01.04.2025 - 30.04.2025
Registration for the implementation has ended.
Timing
22.08.2025 - 30.11.2025
The implementation has not yet started.
Number of ECTS credits allocated
4 cr
Local portion
4 cr
Mode of delivery
Contact learning
Unit
Tradenomi / Tietojenkäsittely (DD)
Campus
Wärtsilä Campus Karjalankatu 3
Teaching languages
Finnish
Seats
10 - 70
Degree programmes
Degree Programme in Business Information Technology
Teachers
Jarmo Talvivaara
Teacher in charge
Jarmo Talvivaara
Groups
DTNS23
Information Technology (BBA), Full-time Studies, Fall, 2023
Course
DT10041

Evaluation scale

H-5

Objective

You know the basics of intelligent automation; key concepts, applications, objectives, opportunities and threats.
You know and are able to apply the possibilities of data-driven machine and deep learning to enable intelligent automation in different application areas (eg. processes, robotics)
You know and are able to apply various intelligent automation platforms, services and tools.
You know and are able to evaluate the suitability of intelligent automation technologies.
You understand the importance of information security in relation to intelligent automation solutions.
You are able to apply solutions that improve information security in the implementation of intelligent automation.

Content

Fundamentals, concepts, benefits and challenges of intelligent automation, applications and technologies.
Intelligent automation environments and platforms; on-premises, cloud services, hybrid solutions.
Artificial intelligence for use in automation; opportunities for machine learning and in-depth learning, as well as application principles.
Basics of deep learning; neural networks (ANN), deep neural networks (CNN), feedback neural networks (RNN), enhanced learning, GANN networks.
Deep learning and automation; cognitive automation machine vision, natural language processing)
Intelligent automation and the possibilities of quantum machine learning (QML); classical computing vs. quantum computing in automation, quantum-classical hybrids, QaaS quantum computing services in automation.
Intelligent automation security.

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