2023-2024

In this module groups of students will be guided to design and develop an entire project in Big Data and AI: from data collection to the final delivery. The students will employ in the project methods, techniques and tools studied in the other modules. The duration of this module, differently from the others, will span across several months until the end of the lectures when the results of the project will be presented in front of a committee.

The master requires an internship to be carried out at one of the partners (companies or institutions) or on the current company a student is working on, on the basis of a well defined project work and under the supervision of a team of tutors composed of instructors and company experts. The internship might require in presence work at the partners' offices or hybrid solutions with smart working.

The course introduces the student to the main concepts of statistical analysis, the methods used and the software implementations to carry out a quantitative and rigorous study of a dataset. After introducing the basic tools of descriptive statistics, the course focuses on probabilistic statistics and its use for data modelling, estimation methods through an inferential approach and statistical hypothesis testing.

The course will deal with time series and spatio-temporal data, in particular mobility. We will illustrate the fundamental characteristics of these two data classes as well as the most common pre-processing and analysis methods. Finally, each lesson will provide examples of use and exercises carried out in Python with the appropriate libraries.

Prerequisites: Data Mining & Machine Learning, Python

Over the past decade, there has been a growing public fascination with the complex “connectedness” of modern society. This connectedness is found in many contexts: in the rapid growth of the Internet and the Web, in the ease with which global communication now takes place, and in the ability of news and information as well as epidemics and financial crises to spread around the world with surprising speed and intensity.

The course introduces the design, implementation and analysis of Information Retrieval systems that are efficient and effective in managing and searching for information stored in the form of collections of texts, possibly unstructured (e.g. Web), and labeled graphs (e.g. Knowledge graph). The theoretical lessons will describe the main components of a modern Information Retrieval system, more exactly of a search engine, such as: crawler, text analyzer, storage and compressed index, query solver, text annotator (based on Knowledge graph and Entity linkers), and rankers.

The module presents the methodological aspects, technologies and systems for designing predictive systems of Artificial Intelligence through machine learning and deep neural networks. The emphasis is placed on the analysis of application problems using examples and case studies, with practical exercises.

Prerequisites: Python & Data Mining & Machine Learning