Green Industrial Engineering for Sustainable Development

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 results obtained. Specific Objectives: Knowledge and understanding: Providing a wide overview of the main machine learning methods and algorithms for the classification, regression, unsupervised learning and reinforcement learning problems. All the problems are formally defined and theoretical basis 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 two homeworks (small projects to be developed at home) allows the 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 results of the homeworks, 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 of results obtained in practical cases given by experts within seminars offered during the course. Learning skills: Self-study of specific application domains, problems and solutions during the homeworks, with possible application of teamwork for the solution of specific problems.