Fundamentals of AI
Niveau
second cycle, Master
Learning outcomes of the courses/module
The participants:
• understand the basics of artificial intelligence
• are able to identify areas of application for artificial intelligence
• are able to create content (text, images, audio) using artificial intelligence
• can assess the advantages and disadvantages of artificial intelligence in the development of smart products
• know restrictions and limitations of artificial intelligence
• understand the basics of artificial intelligence
• are able to identify areas of application for artificial intelligence
• are able to create content (text, images, audio) using artificial intelligence
• can assess the advantages and disadvantages of artificial intelligence in the development of smart products
• know restrictions and limitations of artificial intelligence
Prerequisites for the course
According to admission requirements
Course content
• Overview of terms and definitions
• Basic algorithms and models
• Identification and evaluation of application areas in the context of the product development process of smart products
• Limitations of AI
• Generation and modification of texts
• Generation and modification of images and videos
• Audio generation and modification
• Prompting strategies (e.g. retrieval augmented prompting)
• Use and interaction with AI
• Local vs. Hosted AI Models
• Quality assurance of AI models
• Ethical considerations and implications when using AI models
• Limitations of different models and strategies
• Basic algorithms and models
• Identification and evaluation of application areas in the context of the product development process of smart products
• Limitations of AI
• Generation and modification of texts
• Generation and modification of images and videos
• Audio generation and modification
• Prompting strategies (e.g. retrieval augmented prompting)
• Use and interaction with AI
• Local vs. Hosted AI Models
• Quality assurance of AI models
• Ethical considerations and implications when using AI models
• Limitations of different models and strategies
Recommended specialist literature
• Patrick D. Smith. (2018). Hands-on artificial intelligence for beginners : an introduction to AI concepts, algorithms, and their implementation (1st edition.).
• Géron, A. (2023). Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow: concepts, tools, and techniques to build intelligent systems (Third edition).
• Park, K. R., Kim, E., & Lee, S. (2023). Image and Video Processing and Recognition Based on Artificial Intelligence. (Volume II).
• Géron, A. (2023). Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow: concepts, tools, and techniques to build intelligent systems (Third edition).
• Park, K. R., Kim, E., & Lee, S. (2023). Image and Video Processing and Recognition Based on Artificial Intelligence. (Volume II).
Assessment methods and criteria
Project
Presentation
Presentation
Language
English
Number of ECTS credits awarded
5
Semester hours per week
Planned teaching and learning method
Lecture, individual work with software, group work, presentation and discussion of tasks
Semester/trimester in which the course/module is offered
1