Fundamentals of Artificial Intelligence and Machine Learning
| Structure Type: | Study unit |
|---|---|
| Code: | TK00BR82 |
| Curriculum: | TK 2025 |
| Level: | Bachelor of Business Administration |
| Year of Study: | 2 (2026-2027) |
| Semester: | Autumn |
| Credits: | 5 cr |
| Responsible Teacher: | Rajala, Päivi |
| Language of Instruction: | Finnish |
Learning Outcomes
After completing the course, the student will have a solid understanding of the fundamental principles of generative artificial intelligence, machine learning, and deep learning, as well as their practical application to problem-solving. The student will be capable of analyzing and selecting suitable machine learning models for various tasks and contexts. Through hands-on exercises, the student will learn to design, implement, test, and evaluate simple machine learning models and identify their potential applications across different industries. The course integrates theoretical knowledge with practical skills, providing a strong foundation for developing and utilizing AI-driven solutions.
Student's Workload
135 hours
Contents
* introduction to artificial intelligence: concepts, history, and current state
* principles of machine learning and common algorithms
* deep learning methods and applications
* functionality and applications of generative AI, foundation models
* ethical perspectives and responsible use of AI
* practical exercises with various AI models and tools
* effective prompt engineering
* small-scale project work
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
Up-to-date material specified by the teacher.
Mode of Delivery / Planned Learning Activities and Teaching Methods
Online Learning
Assessment Criteria
The course is assessed on a scale of 0 to 5.
Grade 1–2
The student recognizes the basic concepts of AI and machine learning, but their practical application remains superficial.
Grade 3–4
The student masters the key methods and can select appropriate algorithms for various application contexts. They demonstrate the ability to design, implement, and optimize basic models.
Grade 5
The student shows profound expertise in AI and machine learning techniques, taking into account the scope of the course.
Assessment Methods
Teacher assessment, self-assessment and peer assessment.