Introduction to AI and ML (1 cr)
Code: TX00GB40-3001
General information
- Timing
-
14.04.2025 - 02.05.2025
Implementation is running.
- Number of ECTS credits allocated
- 1 cr
- Mode of delivery
- On-campus
- Unit
- (2019-2024) School of ICT
- Campus
- Vanha maantie 6
- Teaching languages
- Finnish
- Teachers
- Tino Kankkunen
- Minna Kivihalme
- Teacher in charge
- Pekko Lindblom
- Groups
-
DATAINTEL_JOTPAData Intelligence Launchpad - Jotpa hankinta
- Course
- TX00GB40
Objective
This course introduces participants to the basics of artificial intelligence (AI) and machine learning (ML). No prior technical knowledge of the subject is required, but having a general understanding of AI and ML is beneficial. Basic programming skills (Python) and familiarity with SQL are also recommended. The course content is designed for software developers or professionals in similar advanced ICT roles, as well as recent graduates with a bachelor-level degree in the field.
Knowledge:
• Understand the key concepts and principles of data storage, data analytics, and machine learning, as well as their significance in modern software development.
Responsibility and Independence:
• Understand the ethical and societal impacts of data analytics and artificial intelligence and be able to account for them in professional work.
Content
• AI overview
• AI taxonomy
• Computer vision
• Natural language processing
• Document intelligence and knowledge mining
• Generative AI
• Case Company Example
Evaluation scale
0-5
Objective
This course introduces participants to the basics of artificial intelligence (AI) and machine learning (ML). No prior technical knowledge of the subject is required, but having a general understanding of AI and ML is beneficial. Basic programming skills (Python) and familiarity with SQL are also recommended. The course content is designed for software developers or professionals in similar advanced ICT roles, as well as recent graduates with a bachelor-level degree in the field.
Knowledge:
• Understand the key concepts and principles of data storage, data analytics, and machine learning, as well as their significance in modern software development.
Responsibility and Independence:
• Understand the ethical and societal impacts of data analytics and artificial intelligence and be able to account for them in professional work.
Content
• AI overview
• AI taxonomy
• Computer vision
• Natural language processing
• Document intelligence and knowledge mining
• Generative AI
• Case Company Example