Engineering

This course is part of the Laurea Magistrale program. The course is intended for advanced level students only. Enrollment is by permission of the instructor. The course introduces solution algorithms for nonlinear optimization problems that are the basis of many machine learning tools which find applications in telecommunications, electronics, automatic control, and decision support systems. The course is divided into two modules. The first module introduces solution algorithms for nonlinear optimization problems. Topics in this section include: nonlinear optimization: introduction to mathematical programming, models, and algorithms; nonlinear models: unconstrained optimization and constrained optimization; relaxations and penalty algorithms; convex optimization: Lagrangian relaxation and barrier algorithm; and applications of convex optimization to support vector machine and deep learning. The second module introduces basic machine learning techniques for classification and learning. Topics in this section include: algorithms for clustering and classification; neural networks; and laboratory activity on applications for machine learning algorithms arising in real applications.

The course introduces engineering students to the concepts and practices of technological entrepreneurial thinking and entrepreneurship. Using lectures, case studies, business plans, and student presentations, the course teaches life skills in entrepreneurial thought and action that students can utilize in starting technology companies or executing R&D projects in large companies. Major course modules include introduction to entrepreneurship, idea generation and feasibility analysis, and business planning and execution.

This course explores research issues in the newly emerging field of mobile computing. Many traditional areas of computer science and computer engineering are impacted by the constraints and demands of mobility. Examples include network protocols, power management, user interfaces, file access, and security. 

This course emphasizes hands-on laboratory experience and teaches students research background, relevant theories, and basic laboratory techniques relevant to their field of study. Students formulate a research plan, implement it by conducting experiment-based research, and convey the results in scholarly presentations. Students submit a written research report at the end of the course.

This course offers a general overview of nanoscience and nanotechnology and their social, economic, and ethical implications. It discusses the main application fields of nanomaterials and how their introduction into a myriad of products is changing and shaping our lives. Other topics include: consumer perception towards nanotechnology-based products; the main investment areas in nanotechnology at the worldwide level; associated risks, ethical issues, and misconceptions.

The course gives basic theoretical and practical knowledge about database systems and their organization. The emphasis is on relational databases. Topics include an introduction to database systems, basics of the relational model and the query language SQL, methods for data modelling and database design, E/R diagrams, and UML diagrams. Theory for the relational model: functional dependencies, normalization, relational algebra, stored procedures and triggers, and program and web interfaces to databases.  Previous knowledge with programming and Java  required.

This course reviews electric, magnetic, optic, and thermal properties of materials from a view point of classic mechanics and quantum mechanics.