[Dottorcomp] Fwd: programma del corso ML for Non-matrix data

[Luca Franco Pavarino]

Cari Dottorandi,
Per gli interessati, giro il programma del corso intensivo su  ML for
Non-matrix data del prof.  Giacomo Boracchi del PoliMI.
Cordiali saluti,
    Luca Pavarino

---------- Forwarded message ---------
From: Giacomo Boracchi <giacomo.boracchi a polimi.it>
Date: Mon, 1 Jun 2020 at 01:51
Subject: programma del corso ML for Non-matrix dataGiacomo


PhD Course on Machine Learning for Non-Matrix Data,

Politecnico di Milano

Organizers: Giacomo Boracchi, Cesare Alippi, Matteo Matteucci



Overview:

Deep learning models have proven to be very successful in multiple fields
in science and engineering, ranging from autonomous driving to human
machine interaction. Deep networks and data-driven models have often
outperformed traditional hand-crafted algorithms and achieved super-human
performance in solving many complex tasks, such as image recognition.

The vast majority of these methods, however, are still meant for numerical
input data represented as vectors or matrices, like images. More recently,
the deep-learning paradigm has been successfully extended to cover
non-matrix data, which are challenging due to their sparse and scattered
nature (e.g., point clouds or 3D meshes) or presence of relational
information (e.g., graphs). Neural-based architectures have been proposed
to process input data such as graphs and point clouds: such extensions were
not straightforward, and indicate one of the most interesting research
directions in computer vision and pattern recognition.



Mission and goal:

This course aims at presenting data-driven methods for handling non-matrix
data, i.e., data that are not represented as arrays. The course will give
an overview of machine learning and deep learning models for handling
graphs, point clouds, texts and data in bioinformatics. Moreover, most
relevant approaches in reinforcement learning and self-supervised learning
will be presented.



Dates and Teaching Modality