Computer Science

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 provides an introduction to the fundamental concepts and tools needed to understand the emerging role of business analytics in business and non-profit organizations. The course demonstrates how to apply basic business analytics and data science/analytics tools (such as R) to large real-life datasets in different contexts, and how to effectively use and interpret analytic models and results for making better and more well-informed business decisions. This course provides both the organizational and technical aspects of business analytics and serves to provide students with a broad overview of how and why business analytics can be implemented in organizations, and the various approaches and techniques that could be adopted for different organizational objectives and issues.

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.

This course focuses on implementing programs in the imperative paradigm using the C language under a UNIX operating system. It utilizes programming skills, compilation, and debugging aspects. Notions of name scope, lifespan and typing of variables, and recursion are also studied.

This course examines algorithms, tools, practices, and applications of machine learning. Topics include core methods such as supervised learning (classification and regression), unsupervised learning (clustering, principal component analysis), Bayesian estimation, neural networks; common practices in data pre-processing, hyper-parameter tuning, and model evaluation; tools/libraries/APIs such as scikit-learn, Theano/Keras, and multi/many-core CPU/GPU programming.

This course covers basic knowledge of computers, including networks, office software, web basics, and Word, Excel, and PowerPoint in Microsoft Office 2016 packages.