AI002 Building AI

Faculty of Arts
Autumn 2025
Extent and Intensity
0/0/0. 2 credit(s). Type of Completion: z (credit).
Synchronous online teaching
Teacher(s)
PhDr. Petr Škyřík, Ph.D. (seminar tutor)
Mgr. Kateřina Švidrnochová (seminar tutor)
Guaranteed by
PhDr. Petr Škyřík, Ph.D.
Department of Information and Library Studies – Faculty of Arts
Contact Person: Mgr. Alice Lukavská
Supplier department: Department of Information and Library Studies – Faculty of Arts
Prerequisites
ATTENTION: When registering for a course at https://buildingai.elementsofai.com/ you must use your school email, only by its domain can the alumni list be generated regularly.
Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
The capacity limit for the course is 150 student(s).
Current registration and enrolment status: enrolled: 0/150, only registered: 0/150, only registered with preference (fields directly associated with the programme): 0/150
fields of study / plans the course is directly associated with
Course objectives
In collaboration with prg.ai, we are running an international online course that introduces learners to artificial intelligence. AI001 Elements of AI from the University of Helsinki and MinnaLearn in three levels of difficulty: beginner, intermediate, advanced. The course focuses on practical applications of AI and emphasizes critical thinking. Upon completion, you will be able to create and present your own AI project. The course focuses on the following areas: Introduction to AI: Understanding the basic principles and history of artificial intelligence. Different types of AI: weak vs. strong AI. Machine learning: basics of machine learning, supervised and unsupervised learning. Algorithms: linear regression, classification, clustering. Deep learning: basics of neural networks and deep neural networks. Applications of deep learning: image recognition, natural language processing. Practical applications: development and deployment of AI models. Working with data, data preprocessing and feature selection. Ethical and societal aspects: Ethical issues and societal impact of AI. Responsible use of AI technologies. Project assignments: Real-world projects and practical tasks that allow you to apply the concepts you have learned.
Learning outcomes
1. Understanding the basics of artificial intelligence - Understanding what artificial intelligence is and how it affects the modern world - A basic overview of the ethical issues associated with AI 2. Practical applications of AI - Identifying situations where AI can be used effectively - Understanding the basic principles of decision-making algorithms 3. Working with data - Understanding how AI works with data and how data is worked with - The basics of working with big data and using it to train models 4. Fundamentals of algorithms and modeling - Understanding concepts such as model training, model accuracy, generalization and overfitting. - Introduction to machine learning algorithms: supervised learning, unsupervised learning and reinforcement learning 5. Creating and evaluating AI projects - Finding appropriate AI applications for specific problems. - Creating a basic plan for an AI project: defining the goal, collecting data, choosing an algorithm, and measuring success. - Ability to critically evaluate the results of AI models. 6. Ethical and societal issues of AI - Understanding the implications of the use of AI in society. - Identify potential risks such as data distortion, loss of privacy or unintended consequences of automation. 7. Communication skills in AI - Ability to explain basic AI concepts to lay people. - Effectively communicate the results of AI projects and their impact. The course provides practical examples, exercises and final projects that allow you to apply the skills learned in real-world scenarios. It is suitable for beginners and those who want to deepen their knowledge of AI.
Syllabus
  • Building AI is a flexible online course for anyone who wants to learn about the practical methods that make artificial intelligence a reality. You will get a solid introduction to for example machine learning and neural networks, and you will learn where and how AI methods are applied in real life. It is easy to move freely between the three difficulty levels, from multiple choice exercises to programming with Python – depending on whether you know programming or not. As a result of this course, you will be able to craft your own AI idea and present it to the community. This course is divided into following chapters: 1.Getting started with AI 2.Dealing with uncertainty 3.Machine learning 4.Neural networks 5. Conclusion
Teaching methods
homeworks
Assessment methods
In order to pass the course itself, you must complete at least 19 exercises (out of a total of 21) and answer 50% of them correctly. Each exercise has three difficulty options (beginner, intermediate and advanced). Completing any of these levels counts as completing the exercise. The intermediate and advanced exercises require programming. If you complete a sufficient number of intermediate or advanced exercises, this will be indicated on your certificate of completion. At the end of the semester, a reflective questionnaire must be completed for a successful evaluation.
Language of instruction
English
Further comments (probably available only in Czech)
The course is taught each semester.
General note: Kurz je v angličtině dostupný online na https://buildingai.elementsofai.com/, studenti na něm pracují samostatně dle svých možností.
Information on the extent and intensity of the course: asynchronní e-learning.
The course is also listed under the following terms Autumn 2024.
  • Enrolment Statistics (Autumn 2025, recent)
  • Permalink: https://is.muni.cz/course/phil/autumn2025/AI002