Biomedical Signals and Bioimage Analysis
Objective: provide tools for the analysis of images and signals in the biomedical field.
Data Science for Life Sciences
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.
Genomic Data Analytics
Objective: presenting statistical methods and computational analysis techniques in genomics.
Big Data Bioinformatics
Objective: introducing the main algorithmic methods for the storage, compression and analysis of large amounts of biological data, with particular emphasis on the treatment of sequencing data produced with next generation sequencing technologies.
Advanced Algorithms for Data Science
Objective: introducing to the main techniques for the design of algorithms and data structures to manipulate strings, trees and large graphs, in particular to compression techniques and randomization.
NOTE: The equivalent name of this course is "Algorithms for Massive Data".
Stochastic Modelling and Simulation
Objective: introducing students to the fundamentals and practice of stochastic modeling, simulation of stochastic models and inference of parameters starting from observations, with a focus on scalability for large models.
Molecular Simulation
Objective: introducing the computational techniques used in molecular modeling and simulation, and illustrating how these techniques can be employed to describe and/ or predict chemical, physical and biological phenomena.
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.
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.
Bioinformatics and Genomic Data Analytics
Genome sequencing technologies have revolutionised our approach to study biological systems. From healthy tissues to diseases, many questions are approached through the collection of far larger sequencing datasets, and some argue that all modern biology is computational biology. Sequencing technologies can measure molecular states at various degrees of resolution and noise, and complex bioinformatics/ machine learning pipelines are required to extract complex patterns that characterise from basic cellular processes to disease progression.
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.
Computational and Systems Neuroscience
The course aims at introducing the student to modeling and analysis techniques, also data-based, in the area of computational neusciences.
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.
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.
Probabilistic Machine Learning
Objective: presenting advanced machine learning techniques, with a focus on Bayesian methods.