Machine Learning Journal Club - University of Turin

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  • Research Project: the flower paradigm and transformer classifier for EEG data
    The flower paradigm and a transformer classifier for EEG dataThe primary objective of the project is the development of a transformer model for the classification of EEG-related data. Subsequently, our inquiry delves into the crucial aspect of preprocessing, seeking to elucidate its pivotal role in influencing the overall efficacy and performance of the aforementioned architecture. To rigorously examine this dimension, we intend to undertake a systematic comparative analysis. This entails the initial […]
  • Research Project on Continual Learning
    by Luca Bottero Our interests center around the intriguing domain of Continual Learning, a subset of machine learning focused on training models to adapt and evolve over time. This involves enabling these models to learn from new data while retaining previously acquired knowledge. In today’s fast-paced world, characterized by constant changes in data and knowledge, Continual Learning assumes primary importance. Traditional machine learning models often encounter challenges when confronted with novel information, […]
  • Machine Learning for Anomaly detection in Prompt Gamma Spectra
    by Beatrice Villata Tumor treatment can be achieved with radiotherapy techniques by irradiating the target volumes with ionizing radiation. Conventionally, photons are the selected particles for the treatment, but in recent years interest in the use of charged particles has been steadily growing in medical physics. The energy deposition of particle therapy allows to spare the organ at risk around the target volume. At the same time, the energy deposition of particle […]
  • G.Tec BR41N Hackathon 2023
    This weekend 5 of our members (Carola Caivano, Matteo Allione, Matteo Gallo, Marco Casari, Michele Romani) challenged themselves by participating in the hackathon organised by g.tec medical engineering GmbH.It was a formative and motivating experience that allowed us to explore the application of CNNs in the field of Brain computer interface, in particular in P300 speller challenge.The results we gained were very accurate and extremely significant and encourage us to carry […]
  • Outreach event “Machine Learning, Spesa & ChatGPT, cosa hanno in comune”
  • New Machine Learning courses from the MLJC in A.Y. 2022-23
    An outstanding opportunity to learn Machine Learning with an “hands-on” approach and get in touch with the Machine Learning Journal Club association. Introduction to Machine Learning Language: Italian Corso articolato in 10 lezioni di circa 2h tenuto presso il Dipartimento di Fisica in orario 16-18.Dopo una prima introduzione al Machine Learning, il corso si focalizzerà sulla comprensione e l’implementazione di algoritmi in linguaggio Python, dai più semplici quali Linear e Logistic Regression […]
  • MLJC mentioned in TG Regione Piemonte
    On 7th January 2023, RAI TG Regione Piemonte mentioned the Machine Learning Journal Club, its activities and the future Centre for Artificial Intelligence that is expected to be hosted in Turin.

SHARPER – European Researchers’ Night 2021 – Turin

The Machine Learning Journal Club participated in the SHARPER – European Researchers’ Night in Turin on September 24th 2021, organized by the University of Turin and Politecnico of Turin, in the beautiful frame of the Castello del Valentino. The title of the stand was “MACHINE LEARNING E L’INTELLIGENZA ARTIFICIALE DEL FUTURO”. At our stand we presented projects spacing from Brain Computer Interfaces to Scientific Machine Learning.

Brain computer Interfaces

The MedicAI team showcased the new BCI hardware from OpenBCI that will allow the team to run custom experiments in order to acquire data for training, validation and testing of new advanced algorithms for EEG signal classification. These last are fundamental for critical applications, for example in allowing communication or the use of computers for people that cannot use their hands because of diseases. [Open the page]

Scientific Machine Learning

We presented an overview on our past Scientific Machine Learning projects that have the goal to create accurate and efficient models of natural phenomena by merging the knowledge coming from the physics-based approaches using differential equations and the data-driven approaches. In particular we showcased the work “Physics-Informed Machine Learning Simulator for Wildfire Propagation” and our contribution to “NeuralPDE: Automating Physics-Informed Neural Networks (PINNs) with Error Approximations” [Open the page]

Real Time Scientific Machine Learning

We presented an electronics based on a SoC with GPU acceleration , an FPGA and a large amount of inputs and outputs that allows to run machine learning algorithsm not only “offline” on dataset already saved on the hard drive, but also in real time on data coming from sensors. In this way it is possible to deploy machine learning applications where it is required by the model to trigger decisions with a very low latency (thanks to the FPGA-based neural network inference). In this project the final goal is the realization of adaptive control for dynamical systems that can merge knowledge coming from physics priors and sensor fusion. This project is still “work in progress”! [Open the page]


Brain-Computer Interfaces
Real-time Scientific ML

Scientific Machine Learning

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