Instructional goals
During the AI Lab students will work on a complex project with the support of AI tools. The project will require students to define a problem, identify requirements, explore possible solutions, design and implement a prototype, test its behavior, and critically evaluate its strengths and limitations.
Prerequisites
Basic knowledge of Python, linear algebra and calculus.
Intended learning outcomes
At the end of the course, students will better understand the potential of AI tools and their limitations.
Course Contents
The students will be able to choose among a set of proposed project options or, alternatively, suggest their own project idea, subject to approval by the instructor. Projects may take different forms, depending on students’ interests and skills: for example, the development of an application, the design and implementation of a sophisticated algorithmic solution, the creation of a videogame, an agent or the realization of an AI-based tool addressing a concrete problem. In all cases, students will be expected to define clear objectives, motivate their design choices, use AI tools effectively during the development process, and produce a working prototype accompanied by a critical evaluation of the results.
Reference Books
Lecture notes and other course material provided by the instructor and made available on the myluiss platform. Additional references, tutorials, technical documentation, and case studies will be indicated during the course depending on the AI tools used and on the projects developed.
Teaching Methods
Lectures, lab sessions, guided exercises, group activities, and project work. The course adopts a strongly practical and iterative approach: students will be encouraged to experiment with AI tools, compare alternative solutions, and progressively develop a prototype. Active participation during lectures and lab sessions is strongly encouraged.
Assessment Method
Competences will be assessed through group project work, the development of a prototype, the related documentation, and a final presentation.
Thesis assignment criteria
A thesis will be assigned, upon specific request to the instructor, to students who demonstrate a serious and motivated interest in the course topics.
Week 1
Hands-on practice with simple projects to explore the different possibilities.
Week 2
Hands-on practice with simple projects to explore the different possibilities.
Week 3
Deadline for submitting project proposals.
Week 4
Work on the selected project under the supervision of the main instructor and the teaching assistant.
Week 5
Work on the selected project under the supervision of the main instructor and the teaching assistant.
Week 6
Work on the selected project under the supervision of the main instructor and the teaching assistant.
Week 7
Work on the selected project under the supervision of the main instructor and the teaching assistant.
Week 8
Work on the selected project under the supervision of the main instructor and the teaching assistant.
Week 9
Work on the selected project under the supervision of the main instructor and the teaching assistant.
Week 10
Work on the selected project under the supervision of the main instructor and the teaching assistant.
Week 11
Work on the selected project under the supervision of the main instructor and the teaching assistant.
Week 12
Project presentations. Prototype demos and critical discussions.