Statistical Machine Learning
Objective: presenting advanced machine learning techniques, with a focus on deep learning and Bayesian methods.
Detailed description (a.a. 2017/18): See here.
Data Science for Healthcare
Probabilistic Machine Learning
Objective: presenting advanced machine learning techniques, with a focus on Bayesian methods.
Health Data Analytics
Objective: introducing advanced computational and statistical techniques for the analysis of clinical data.
Software Development Methods
Objective: introducing concepts and techniques for collaborative development of large and complex software systems for industrial applications, including Java, software development lifecycle, best practices in software development as code testing, versioning, and design patterns.
Management of Health Data
Objective: providing expertise for the management of clinical and biomedical data from computerized medical records, through the methods of health information technology and of process modeling.
Biomedical Signals and Bioimage Analysis
Objective: provide tools for the analysis of images and signals in the biomedical field.
Computer Vision and Pattern Recognition
Objective: introducing machine learning techniques for artificial vision and pattern recognition in sequential data.
Advanced Programming and Algorithmic Design
Objective: providing advanced knowledge of both theoretical and practical programming in C / C ++ and Python, with particular regard to the principles of object oriented programming and best practices of software development (advanced use of version control systems, continuous integration, unit testing), and introducing the modern technology of algorithms development.
Optimization Models
Objective: providing students with the methodological, theoretical and practical tools to formulate linear programming models and combinatorial optimization problems and to solve them, even for high dimensionality problems, using appropriate optimization software.
Open Data Management and the Cloud
Objective: providing students with practical information on how to design data models and data structures, to manage metadata to optimize access and research, and to become familiar with interoperability standards. The course will focus on the concept of open data, with efficiency for big data projects, and the concept of cloud as an infrastructure for data management and their processes.
Introduction to Machine Learning
Objective: Introduce the students to the machine learning fundamentals, to the main techniques on supervised learning, and to the principal application domains. Present evolutionary calculation. The course explains how to design, develop and evaluate simple ML-based end-to-end systems and, at the same time, how to describe their operations.
Data Analytics and Statistical Learning
Objective: introduce the student to the principles of learning from data based on statistics, and to the scientific treatment of data to obtain new and reproducible knowledge. Some of the main supervised and unsupervised statistical learning techniques are presented.
Foundations of High Performance Computing
Objective: introducing students to modern architectures for high performance computing. Students will learn how to properly test such architectures (computing power, bandwidth, latency, energy efficiency). Leveraging these skills, students will be introduced to the parallel programming based on MPI protocols (Message Passing Interface) and multi-threading with OpenMP.
Information Retrieval and Data Visualization
Objective: presenting the most important conceptual aspects of information retrieval systems and both principles and techniques for data visualization.
Machine Learning and Data Analytics
Objective: introducing students to the principles of data analysis, to data mining, and to machine learning (supervised and unsupervised learning).
Detailed description (a.a. 2017/18): See here.
Natural Language Processing
Objective: introduce modern computational techniques and machine learning techniques for the analysis of natural language.
Numerical Analysis
Objective: providing numerical analysis tools for scientific computing, with particular attention to linear algebra, polynomial approximation, numerical integration, numerical solution of ordinary differential equations and partial differential equations, approximation of eigenvalues and eigenvectors.
Data Management for Big Data
Objective: introducing students to computational management of data, in particular the characterization of an information system, data modeling, design and management of databases, including non-traditional ones (eg, unstructured documents, spatial data, biological data , multimedia data), to the fundamentals of distributed data and to methodologies and techniques for the management and analysis of big data.
Statistical Methods for Data Science
Objective: presenting the basic elements and principles of inferential statistics and statistical techniques for the analysis of complex data.