Statistics

The course is designed to equip students with experience, knowledge, and skills for succeeding in globally interdependent and culturally diverse workplaces. During the course, students are challenged to question, reflect upon, and respond thoughtfully to the issues they observe and encounter in the internship setting and local host environment. Professional and personal development skills as defined by the National Association of Colleges and Employers (NACE), such as critical thinking, teamwork, and diversity are cultivated. Assignments focus on building a portfolio that highlights those competencies and their application to workplace skills. The hybrid nature of the course allows students to develop their skills in a self-paced environment with face-to-face meetings and check-ins to frame their intercultural internship experience. Students complete 45 hours of in-person and asynchronous online learning activities and 225-300 hours at the internship placement.

This course is part of the Laurea Magistrale degree program and is intended for advanced level students. Enrolment is by permission of the instructor. At the end of the course, the student has a deep knowledge of industrial applications that benefit from the use of machine learning, optimization, and simulation. The student has a domain-specific knowledge of practical use cases discussed in collaboration with industrial experts in a variety of domains such as manufacturing, automotive, and multi-media. The course is primarily delivered as a series of simplified industrial use cases. The goal is to provide examples of challenges that typically arise when solving industrial problems. Use cases may cover topics such as: anomaly detection; Remaining Useful Life (RUL) estimation; RUL based maintenance policies; resource management planning; recommendation systems with fairness constraints; power network; management problems; epidemic control; and production planning. The course emphasizes the ability to view problems in their entirety and adapt to their peculiarities. This frequently requires to combine heterogeneous solution techniques, using integration schemes both simple and advanced. The employed methods include: mathematical modeling of industrial problems; predictive and diagnostic models for time series; Combinatorial Optimization; integration methods for Probabilistic Models and Machine Learning; integration methods for constraints and Machine Learning; and integration methods for combinatorial optimization and Machine Learning. The course includes seminars on real-world use cases, from industry experts. The course contents may be (and typically are) subject to changes, so as to adapt to some degree to the interests and characteristics of the attending students.

The reliability of the findings from a research study depends critically on the design of the study. An understanding of the principles of study design is important for all consumers of scientific research, and essential for all those who will be carrying out scientific research. This course provides students with the knowledge and skills to translate a research aim into specific study objectives, construct a study design to address the objective(s), and write a plan for the statistical analysis. Students will also learn skills in critical evaluation of published research papers. Topics include survey methods, experimental and observational studies, measurement, control of confounding and bias, evaluation of competing designs, determination of study size.

This course provides research training for exchange students. Students work on a research project under the guidance of assigned faculty members. Through a full-time commitment, students improve their research skills by participating in the different phases of research, including development of research plans, proposals, data analysis, and presentation of research results. A pass/no pass grade is assigned based a progress report, self-evaluation, midterm report, presentation, and final report.