Objectives and competences
The student will:
• Understand the basics of artificial intelligence and machine learning.
• Understand the role, meaning, strengths and weaknesses of ML models.
• Define appropriate methods for problem solving.
• Understands different ML algorithms.
• Use ML tools and SW..
• Evaluate quality of learnd ML models.
• Present the results of modeling.
• Understand the concept of a digital double, know examples of the use of machine learning in the context of digital doubles.
• Understand the legal and ethical aspects of using AI in business.
Content (Syllabus outline)
1. Introduction to artificial intelligence and machine learning
2. The role of machine learning in organizations
3. Machine learning basics
4. Machine learning algorithms (various methods, how they work, strengths, weakness, use)
5. Classification & Regression models
6. Clustering
7. Texts and visual analysis
8. Collecting data from instruments and devices (how to collect, save, analyse data from different sources)
9. Data pre-processing
10. Finding patterns and outliers in (big) data
11. Model development
12. Experimental evaluation of the learned models
13. Use cases of machine learning models
14. Digital duplicate technology as a basis for advanced data analytics
15. Machine learning methods for achieving the Sustainable Development Goals
16. legal and ethical frameworks for the use of artificial intelligence
Learning and teaching methods
• Lectures
• Team work
• Case studies
• Excersises
Intended learning outcomes - transferable/key skills and other attributes
Knowledge and understanding of:
• At the end of the course, the students will be able to:Define the problem and its context.
• Identify opportunities for application of data visualization.
• Acquire data from available sources.
• Pre-process data.
• Choose appropriate methods with emphasis on data visualization.
• Build ML model using appropriate tools (Orange, Tensorflow).
• Interpret the results.
• Experimentally valuate the quality of learned ML models.
• Effectively communicate modelling results to various audiences.
Readings
1. Provost, F., & Fawcett, T. (2013). Data science for business: [what you need to know about data mining and data-analytic thinking] (1st ed., str. XXI, 386). O’Reilly.
2. Neapolitan, R. E., & Jiang, X. (20182020, cop.). Artificial intelligence: with an introduction to machine learning (2nd ed., str. XIII, 466). CRC Press, an imprint of Taylor & Francis Group.
Kononenko, I., & Robnik Šikonja, M. (2010). Inteligentni sistemi (1. izd., str. XI, 366). Založba FE in FRI.
Prerequisits
Basics computer skills.
Conditions for exam admission:
Completed assignments at lectures and tutorials.
Completed project.
Additional information on implementation and assessment coursework (20%)
Project (30%)
Written exam (50%)
