Shoot for the moon. Even if you miss, you'll land among the stars.

The goal of the Bachelor equivalent and Master equivalent degree in Computer Engineering is to train professional workers to be able to use computer science tools to address the issues that arise during the design, the sizing, and the management of the computer and information processing systems.

Therefore, we require the student to elaborate also on the study of automation, digital electronics, power electronics, and of telecommunications systems, in order to give a viewpoint oriented to the "problem solving".

The 'Enterprise Mobile Application Development' course focuses on the development of multiplatform mobile applications. Prerequisites: web programming skills (HTML, CSS and Javascript) acquired in other courses.

Course contents:

• Mobile web applications, native and hybrid
• Cordova hybrid application framework
• Angular 5
• Ionic framework v3

Overview of enterprise applications, Customer Reationship Management, Supply Chain Management. Design and development of a multi-platform mobile application as part of a group project.

This is what I've learned:

• Generate IoT concepts and design IoT solutions within your area of expertise.
• Map out the process for an IoT solution, and identify the sensors and other devices required.
• Evaluate different infrastructure components and network systems, and design the basic network for your IoT ideas.
• Apply software solutions for different systems and Big Data to your concept designs, and appreciate how data is managed in the network.
• Identify and analyse IoT security and privacy risks, and concept design secure hardware and software.
• Produce a viable IoT concept design that solves a problem, is ready to prototype and test, and has an identified route to market.

This course provides a broad introduction to machine learning, datamining, and statistical pattern recognition. Topics include: (i) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks). (ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning). (iii) Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI). The course will also draw from numerous case studies and applications, so that you'll also learn how to apply learning algorithms to building smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas.

The community of young creatives and best talents of digital and cultural innovation, selected and trained by Giffoni Hub.

Through Open Innovation pop-up labs taking place in various locations around the world, Dream Team’s young people gather and work together to devise, design and realize strategies, prototypes and services intended for big companies and institutions.