Fully Booked
The course aims to provide a comprehensive and integrated overview of modern High-Performance Computing (HPC) and Artificial Intelligence technologies. It guides participants from the operational foundations of HPC environments through to the development, training, and deployment of advanced Machine Learning, Deep Learning, and Large Language Models.
By the end of the program, participants will be able to: Design and execute AI pipelines on supercomputing infrastructures; Select appropriate models and techniques based on available data; Critically evaluate the performance of developed models.
Furthermore, the course intends to foster autonomy in the practical use of HPC tools and the application of key modern AI paradigms, including multimodal models.
Course Content and Programme
Module 1 – HPC
Intro to Development Environments and Bash (if required): Setting up the development environment (IDE), managing environments, and Bash scripting basics.
Intro to HPC: How the Leonardo supercomputer works, how to request resources, connecting via SSH, data transfer (upload/download), and job submission.
4 h
Module 2 – Machine Learning
Preprocessing: How to prepare data for a machine learning pipeline to maximize information extraction.
Supervised and Unsupervised Learning: How to select the ideal model based on the available data.
Regression and Classification: Techniques for addressing these two major categories of problems.
Models: A deep dive into available models, focusing on which to use in specific scenarios and why.
4 h
Module 3 – Neural Networks
Neural Networks: Introduction to neural networks—what they are, how they are built, and the problems they can solve.
Computer Vision: Specific models for artificial vision, implementation strategies, and selection criteria.
Training: Training a model, understanding batches, data augmentation, the training loop, loss functions, and optimizers.
Evaluation: How to monitor model progress and measure performance metrics.
4 h
Module 4 – LLMs and Multimodal Models
LLMs: Introduction to Large Language Models—what they are and how they function.
Fine-tuning: How to adapt a pre-trained model to meet specific needs.
Inference: Using a model to generate predictions and identifying the best tools for production deployment.
Multimodal Models: Introduction to multimodal models, their inner workings, and practical use cases.
4 h
Course Duration
16 hours
Target Audience
The course is designed for professionals seeking to gain a deeper understanding of the core themes in Artificial Intelligence and their potential applications within a corporate environment.
Requirements
Basic knowledge of programming, math and statistics.
Calendar
May 12, 2026 from 9:00 to 13:00
May 25, 2026 from 9:00 to 13:00
June 8, 2026 from 9:00 to 13:00
June 22, 2026 from 9:00 to 13:00