The event will be held between the 17th and 18th of April 2021, as part of the BCI and Neurotechnology Spring School. MLJC will attend with five teams. We will have the opportunity to present our work and our association in front of an international audience.
All the results will be available open-source on our GitHub.
We are a group of students from the University of Turin and the Polytechnic University of Turin, mostly from a STEM background.
We are passionate about machine learning and artificial intelligence, thus we created a community where to share our geeky ideas and our expertise.
We are actively engaged in research projects with a broad, interdisciplinary focus and participating in hackathons / competitions related to AI.
If you’re interested in taking part in any way in our research projects or learning activities, feel free to drop an email at: info(at)mljc.it
The aim of our association is to promote hands-on knowledge of machine learning and artificial intelligence across the academic and civic landscape of Turin.
We believe interdisciplinarity and cross-contamination can be the key to new discoveries and fruitful, engaging ideas.
We try to innovate learning and research paradigms with a focus on self-organization and peer-learning / peer-reviewing, ultimately producing industry-grade science.
In order to promote machine learning literacy we jump-started a wide set of projects, on various premises and online:
We started our first activities as a little group of students from the M.Sc. in Physics of Complex Systems at the University of Turin.
We were curious about emergent possibilities stemming from Data Mining, Machine Learning and Artificial Intelligence: that’s why we started experimenting with real-world projects and participating in some Kaggle competitions we were passionate about (for example, we took part in the Deep Fake Detection Challenge).
Going forward, we decided to open our group to students from different backgrounds: in order to build some common knowledge, we started organizing extra-curricular lessons and activities, covering Python applied to machine learning (you can find the course material here).
As we meet and engage with new people from all walks of science, our community grows large and diverse: we are proud to count members from computer science, engineering, mathematics, biology, economics and neuropsychology to name a few.
We meet regularly (online or in person), to work and organize projects related to Machine Learning and its applications. You can find more information about our projects here.
MLJC presented the following research at AAAI-MLPS Symposium 2021 on February 2021. Please find the proceedings below or in the official website: https://sites.google.com/view/aaai-mlps/proceedings?authuser=0
The aim of this work is to evaluate the feasibility of re-implementing some key parts of the widely used Weather Research and Forecasting WRF-SFIRE simulator by replacingits core differential equations numerical solvers with state-of-the-art physics-informed machine learning techniques tosolve ODEs and PDEs, in order to transform it into a real-time simulator for wildfire spread prediction. The main programming language used is Julia, a compiled language which offers better perfomance than interpreted ones, providing Justin Time (JIT) compilation with different optimization levels. Moreover, Julia is particularlywell suited for numerical computation and for the solution ofcomplex physical models, both considering the syntax and thepresence of specific libraries such as DifferentialEquations.jl and ModelingToolkit.jl.