Intelligent Systems and Applications
A.Y. 2026/2027
Learning objectives
The aim of the course is to provide students with the theoretical and practical knowledge required to understand, design, and implement intelligent systems based on artificial intelligence methods.
Expected learning outcomes
By the end of the course, students will be familiar with the most widely used artificial intelligence methods and techniques applicable to the development of intelligent systems, as well as the main methodologies for their implementation and integration into software applications.
Lesson period: Second semester
Assessment methods: Esame
Assessment result: voto verbalizzato in trentesimi
Single course
This course can be attended as a single course.
Course syllabus and organization
Single session
Responsible
Lesson period
Second semester
Course syllabus
The course will focus on the main artificial intelligence models and algorithms, with particular emphasis on the design and development of intelligent systems for processing data acquired in heterogeneous application contexts.
The following topics will be covered:
- Fundamentals of artificial intelligence and intelligent systems
- Artificial intelligence processing pipelines
- Artificial neural networks
- Machine learning methods for regression and classification
- Deep learning
- Convolutional neural networks, Transformers, and Vision Transformers
- Semantic segmentation and other methods for multidimensional signal processing
- Design and implementation of software for intelligent systems
- Application examples
The following topics will be covered:
- Fundamentals of artificial intelligence and intelligent systems
- Artificial intelligence processing pipelines
- Artificial neural networks
- Machine learning methods for regression and classification
- Deep learning
- Convolutional neural networks, Transformers, and Vision Transformers
- Semantic segmentation and other methods for multidimensional signal processing
- Design and implementation of software for intelligent systems
- Application examples
Prerequisites for admission
None
Teaching methods
Lectures
Teaching Resources
Papers and slides available on the course web site
Assessment methods and Criteria
The assessment to evaluate the students' knowledge and understanding of the subject consists of a written exam. The exam includes theory questions and exercises and is closed-book. The mark is expressed in thirtieths and the grading will consider the correctness, completeness, and clarity of the answers to the questions and exercises. A minimum score of 18 is required to pass. An additional oral exam may be required if deemed necessary.
Professor(s)