This course has a threefold objective: 1) to introduce to the student to the 'cultural' and technological issues related to AI and machine learning (ML) 2) to teach the use of machine learning techniques as a way to solve real problems 3) to gain practice implementing AI and ML algorithms and getting them to work for practical field of application

The student must acquire three fundamental types of knowledge: 1) the understanding of basics concepts (theory and models) of AI and ML 2) the ability to solve concrete problems applying ML algorithms using interactive frameworks and SW libraries 3) the ability to modify and adapt template ML algorithms for a new purpose These skills will be applied to the solution of practical problems.

No special prerequirements are expected except some basics of programming language acquired in the course "Computer programming in Python".

THEORY [40 HRS] - Hello AI and ML. We introduce the core idea of teaching a computer to learn models using data without being explicitly programmed. - Background Concepts, Probability and Linear Algebra Review - Representation of numerical and non-numerical data - Linear Regression - Neural Networks Representation and Learning - Support Vector Machines - Unsupervised Learning vs Supervised Learning - Dimensionality Reduction - Application Examples LABS [21 HRS] - Software Development Framework Tutorial - Several assignments designed to help you understand how to implement the learning algorithms in practice.

The course includes about 21 hours of lab, in which the topics covered in the classes will be implemented using the development framework.

Each week the student will attend 3 hours of lectures and 1.5 hours of exercises and laboratories. The lessons will be divided into different types: - Theory: illustration of theoretical topics (models, algorithms) with examples - Problem solving: analysis of real problems and benchmarks, classroom discussion, design/adaptation of ML techniques to solve real applications - Practice: illustration of the main ML modules available in the development framework and their demonstration using the PC

- Handouts of class material. - On-line material with references provided during the course.

Slides; Esercizi risolti; Esercitazioni di laboratorio;

Modalità di esame: Prova orale facoltativa; Prova scritta in aula tramite PC con l'utilizzo della piattaforma di ateneo;

Exam: Optional oral exam; Computer-based written test in class using POLITO platform;

... The exam will consist of two parts: 1) A set of assignments addressing specific ML tasks to be developed during the course. For each assignment, instructors will provide a dataset and the students will have to develop analysis pipelines to solve the assigned problem (based on the topics presented during lectures and labs). Then, students should write a technical report detailing the employed methodology and a critical analysis of the obtained results. Both the designed pipeline and the report will be submitted by students to the instructor for evaluation. 2) A written test that aims at assessing the student's knowledge of the theoretical aspects of the course (through practical exercises and open-answer questions) and the ML design skills (through the designing of analysis pipelines that implement the solution of a practical problem for which datasets are provided). The exam will assess the developed skills of the candidate to solve specific problems by applying the knowledge acquired during the course in the design of ML pipelines of data analysis. The duration of the written test is 1.5 hours and it is a closed book test. The maximum score for the written test is 30 cum laude. During the discussion of the results of the written test, the instructor can request a supplementary oral test that can cover the whole course program and is meant to assess and elaborate the student's skills.

Gli studenti e le studentesse con disabilità o con Disturbi Specifici di Apprendimento (DSA), oltre alla segnalazione tramite procedura informatizzata, sono invitati a comunicare anche direttamente al/la docente titolare dell'insegnamento, con un preavviso non inferiore ad una settimana dall'avvio della sessione d'esame, gli strumenti compensativi concordati con l'Unità Special Needs, al fine di permettere al/la docente la declinazione più idonea in riferimento alla specifica tipologia di esame.