Abstract: The course is a theoretical–practical introduction covering the fundamentals of modern machine learning: problem formulation (classification, regression, ranking), preprocessing pipelines, evaluation (metrics and experimental protocols), regularization, optimization, linear models, trees, ensembles, and an introduction to neural models for deep learning. Particular attention is given to good experimental practices, data management, and principles for making experiments reproducible, including practical guidance on using the newly acquired machines: copying and synchronizing code via GitHub on the purchased machines, and creating and managing isolated Python environments (virtualenv/conda) to ensure reproducibility and consistency of software installations.

Abstract: A practical course aimed at teaching the PyTorch ecosystem: tensors and operations, building custom models, managing datasets and dataloaders, optimization strategies, debugging, saving and loading checkpoints, acceleration techniques (mixed precision), and integration with monitoring tools (TensorBoard, Weights & Biases). Special attention will be dedicated to using the GPUs of the purchased machines: managing multiple devices in PyTorch, memory allocation and best practices for CPU↔GPU data transfer, and using torch.cuda for profiling.

Abstract: A course focused on PyTorch Lightning as an abstraction layer to accelerate and make GPU training reproducible: structure of the LightningModule and Trainer, checkpointing, callbacks, logging, learning-rate scheduling strategies, and job orchestration (e.g., batch scripts / scheduler usage). Emphasis will be placed on the correct use of the GPUs available on the purchased machines, particularly the use of multiple GPUs within a single machine, with practical examples on configuration, profiling, and best practices to ensure stability and efficiency of experiments.

Abstract: A practical course aimed at applying the technologies learned in the previous modules to a business case, with the involvement of tech startup founders. The first week begins with an introduction to what it means to build a startup, focusing on reasoned selection of an industrial domain in which to apply the technology, analysis of the problem faced by a specific group of users, and identification of the solution offered. The second week encourages participants to actually interact with potential customers, starting to study their behavior and presenting them with the solution concept.