Machine Learning for Intelligent Systems

‹ Cognitive, Social and Economic Data Analytics Big Data Management and Analytics ›

An ever-growing number of ICT-based systems rely on the so-called “operational intelligence”, which is about inferring important facts from heterogeneous data sources, generated by humans, e.g., vehicular mobility, space occupation in smart buildings, or by connected sensing devices within smart cities, autonomous driving, and smart digital health scenarios, to name a few.

Such data is exploited to analyze complex physical world processes and systems and make critical decisions at societal and business levels. For such reasons, smart data analysis and decision-making tools are becoming the most important asset for cutting-edge ICT-based companies and organizations.

The Machine Learning for Intelligent Systems curriculum offers technical training on advanced AI/ML theories and tools, efficient data handling (“big data”), software implementation, and utilization to solve real-world issues.

Click here to find out the details of the MLIS program study plan!

Requirements

We welcome students with a basic background in statistics, algebra, algorithms, and stochastic processes. Most importantly, prospective students should have an attitude toward applied mathematics and a keen interest in using, conceiving, and programming algorithms to tackle real-world problems.

What to expect

This interdisciplinary curriculum offers plenty of technical training on “machine learning”, “deep and statistical learning”, “data mining”, and “big data analysis”. These will be complemented by transversal courses on “law and data”, “cognition and computation”, and by a number of application-oriented courses, where students will be confronted with relevant real-world applications and use cases.

This student profile embodies a data scientist with ample knowledge in applied mathematics, empowered with the ability to solve real-world issues, as well as understanding the implications of dealing with data at societal and business levels.

This data scientist will be able to extract, analyze and interpret large amounts of data using algorithmic, AI/machine learning, and statistical tools, making it accessible to businesses.

Employment prospects

Modern companies are collecting an increasing amount of data from their businesses (ICT networks, smart and connected cities, customers and markets, etc.). Data analysis and online decision-making are becoming key technological assets.

This data scientist profile is in high demand across a number of sectors such as finance, transportation, logistics, ICT products and businesses, retail/e-commerce, government, and scientific research, to name a few. Employment prospects are countless.