Training course “Artificial Intelligence – Level I”

Introduction

The course, consisting of two different modules, aims to provide an overview of the world of Artificial Intelligence, closely linked to the enabling technology of supercomputing (HPC).

The first module will provide a general overview of the main Machine Learning models, combining theoretical explanations with practical exercise sessions to empower participants in the use of such algorithms.
The second module will provide the basic knowledge for using an HPC machine in order to understand its potential within the application discussed in the first module. Subsequently, some use cases will be provided to motivate the use of HPC in the field of Machine Learning. In a practical session, a machine learning model will also be implemented on an HPC infrastructure.

Target Audience

The training program is dedicated to individuals with a technical-scientific background, basic programming language skills, and familiarity with the Linux operating system. They should be interested in exploring and experimenting with advanced Machine Learning techniques using HPC.

Different types of data
Key issues related to data
Machine Learning models (KNN, SVM, logistic regression, random forest)
Basic concepts of HPC

Instructors

Domitilla Brandoni
TBD

Information and Accessibility

The course will take place over two days in person (duration: 12 hours). Dates and Schedule TBD

Course Program

Duration: 12 hours, spread over 2 days

Module 1

Objectives: The first module will focus on introducing the main concepts related to supervised and unsupervised machine learning, as well as implementing the described models using the Python language.
Contents: (1) Introduction to key data issues; (2) Supervised and unsupervised machine learning models; (3) Practical exercises using the described models in Python.

Module 2

Objectives: The first part will provide some basic knowledge in the field of HPC. In a practical session, participants will learn how to interact with a supercomputer (login, data loading, code execution). The second part will describe some use cases to motivate the use of HPC in ML. In a practical session, a machine learning model will be implemented on an HPC infrastructure.
Contents: (1) Introduction to HPC; (2) Practical exercises in an HPC environment; (3) Practical exercises on machine learning models in an HPC environment.