Engineering in Computer Science and Artificial Intelligence - Ingegneria Informatica e Intelligenza Artificiale

General Objectives. The objectives of this course are to present a wide spectrum of Machine Learning methods and algorithms, discuss their properties, convergence criteria and applicability. The course will also present examples of successful application of Machine Learning algorithms in different application scenarios.The main outcome of the course is the capability of the students of solving learning problems, by a proper formulation of the problem, a proper choice of the algorithm suitable to solve the problem and the execution of experimental analysis to evaluate the obtained results. Specific Objectives. Knowledge and understanding: Providing a wide overview of the main machine learning methods and algorithms for classification, regression, and unsupervised learning learning problems. All the problems are formally defined and theoretical bases as well as technical and implementation details are provided, in order to understand the proposed solutions. Applying knowledge and understanding: Solving specific machine learning problems starting from training data, through a proper application of the studied methods and algorithms. The development of small projects to be developed at home allows students to apply the acquired knowledge. Making judgements: Ability of evaluating performance of a machine learning system using proper metrics and evaluation methodologies. Communication skills: Ability of writing a technical report describing the adopted solution, thus showing abilities in communicating results obtained from the application of the acquired knowledge in solving a specific problem. Being exposed to examples of communication by discussing results obtained in practical cases. By working in team for home projects, students will learn how to effectively communicate at a technical level. Learning skills: By acquiring the basic vocabulary and the fundamentals of Machine Learning, students will develop the required skills to autonomously access specialised literature and learn new approaches and techniques, useful to carry out home projects. More in general, the course provides the basics required to successfully learn more advanced ML topics, such as Deep Learning and NLP, typically offered in advanced academic courses.