Fundamentals of AI
Niveau
Learning outcomes of the courses/module
The students:
* 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
Course content
* Basics of artificial intelligence
- Overview of terms and definitions
- Basic algorithms and models
* Application areas of AI
- Identification and evaluation of application areas in the context of the product development process of smart products
- Limitations of AI
* Applications of generative AI
- Generation and modification of texts
- Generation and modification of images and videos
- Audio generation and modification
- Prompting strategies (e.g. retrieval augmented prompting)
* Implementation of AI
- Use and interaction with AI
- Local vs. Hosted AI Models
- Quality assurance of AI models
* Restrictions and limitations
- Ethical considerations and implications when using AI models
- Limitations of different models and strategies
- Overview of terms and definitions
- Basic algorithms and models
* Application areas of AI
- Identification and evaluation of application areas in the context of the product development process of smart products
- Limitations of AI
* Applications of generative AI
- Generation and modification of texts
- Generation and modification of images and videos
- Audio generation and modification
- Prompting strategies (e.g. retrieval augmented prompting)
* Implementation of AI
- Use and interaction with AI
- Local vs. Hosted AI Models
- Quality assurance of AI models
* Restrictions and limitations
- 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 work and presentation
Language
English
Number of ECTS credits awarded
5
Share of e-learning in %
20
Semester hours per week
2.5
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
Name of lecturer
Prof (FH) DI. Thomas Schmiedinger, PHD
Academic year
Key figure of the course/module
DIT.4
Type of course/module
integrated lecture
Type of course
Compulsory