Study Guide

Objectives

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.

Content

• 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

Assessment criteria, satisfactory (1-2)

The student recognizes the basic concepts of AI and machine learning, but their practical application remains superficial.

Assessment criteria, good (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.

Assessment criteria, excellent (5)

The student shows profound expertise in AI and machine learning techniques, taking into account the scope of the course.