AI Tools
| Structure Type: | Study unit |
|---|---|
| Code: | TK00BR83 |
| Curriculum: | TK 2025 |
| Level: | Bachelor of Business Administration |
| Year of Study: | 2 (2026-2027) |
| Semester: | Autumn |
| Credits: | 5 cr |
| Responsible Teacher: | Ulvinen, Tero |
| Language of Instruction: | Finnish |
Learning Outcomes
The student will become familiar with AI tools and model operations, as well as their utilization in practical applications. The student will learn key tools, such as using AI frameworks and models at a general level, and will be introduced to various development environments, including local models, cloud services, and API interfaces. The course focuses on the fundamentals and application of tools and frameworks like TensorFlow, PyTorch, and large language models (LLMs). Students will also learn to evaluate model performance and leverage them innovatively in their own projects. The course emphasizes practical skills, ethical responsibility, and the application of AI solutions to various business and technological needs.
Student's Workload
135 hours
Prerequisites / Recommended Optional Courses
-
Contents
AI Tools and Frameworks: TensorFlow, PyTorch, and their applications
Foundations and Practical Utilization of Large Language Models (LLMs)
Introduction to Development Environments: Cloud services, API interfaces, and their integration
Evaluating and Optimizing Model Performance in Various Application Contexts
Hands-on Exercises with AI Tools: Processing text, images, audio, and data
Ethical Considerations and Responsibility in the Use of AI Tools
Regional Impact
The course considers the demands of the local business sector and enhances regional competence.
Internationality
The course considers the international aspects of companies and organizations in the region.
Recommended or Required Reading and Other Learning Resources/Tools
Current materials announced by the instructor.
Mode of Delivery / Planned Learning Activities and Teaching Methods
Online Learning
Assessment Criteria
Grade 1–2
The student recognizes the key AI tools and has a superficial understanding of their basic principles. They can utilize ready-made model examples under guidance, but demonstrate little independent optimization.
Grade 3–4
The student demonstrates the ability to select appropriate AI tools for different application contexts and masters their basic configuration (e.g., parameter tuning). They can evaluate model performance and apply what they have learned to handle various data types.
Grade 5
The student seamlessly integrates multiple AI tools into large-scale projects, optimizing model performance innovatively. They show an in-depth understanding of the models’ limitations and potential, delivering versatile, scalable solutions for both business and technological needs.
Assessment Methods
Teacher assessment, self-assessment and peer assessment.