Description:

The increasing amount of scientific data collected through sensors or computational simulations can take advantage of new techniques for being processed in order to extract new insights out of raw data. The purpose of this one-week school is to present researchers and scientists with methods, tools and techniques for exploring and mining, large data sets using Cineca high performance resources. The school is an introductory set of lectures aimed at training beginner participants in the application of relevant statistical, machine and deep learning algorithms to create classification and predictive models using Cineca resources to execute efficient processing jobs. The school will consist of introductory lectures held by data scientists, and hands-on sessions. Furthermore some practical insights on a few use cases addressed in the field of reaseach projects in Cineca will be introduced.

Skills:

At the end of the course, the student will possess and know how to use the following skills:
- Use of Cineca HPC resources
- Python basic programming for ML
- Machine Learning algorithms and libraries
- Deep Learning frameworks

Target audience:

Young students, PhD, and researchers in computational sciences and scientific areas with different backgrounds, looking for new technologies and methods to process and analyse large amount of data.

Pre-requisites:

Participants must have basic knowledge in statistics, fundamentals of computer programming with Python and use of GNU/Linux-based systems.