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Regularization Methods for Machine Learning 2021


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At a glance

Duration

20 hours


Instructors

When

Jun 21 2021 , Jun 25 2021


Where

either Teams or Via Dodecaneso 35, Genova, Italy


Important Dates

Application deadline: Mar 31
Notification of acceptance: May 1


Abstract

Understanding how intelligence works and how it can be emulated by machines is an age old dream and arguably one of the biggest challenges in modern science. Learning, with its principles
and computational implementations, is at the very core of this endeavor.


Recently, for the first time, we have been able to develop artificial intelligence systems able to solve complex tasks considered out of reach for decades.

Modern cameras recognize faces, and smart phones voice commands, cars can see and detect pedestrians and ATM machines automatically read checks.
In most cases at the root of these success stories there are machine learning algorithms, that is, software that is trained rather than programmed to solve a task.


Among the variety of approaches to modern computational learning, we focus on regularization techniques, that are key to high-dimensional learning.

Regularization methods allow to treat in a unified way a huge class of diverse approaches, while providing tools to design new ones. Starting from classical notions of smoothness, shrinkage and margin, the course will cover state of the art techniques based on the concepts of geometry (aka manifold learning), sparsity and a variety of algorithms for supervised learning, feature selection, structured prediction, multitask learning and model selection. Practical applications for high dimensional problems, in particular in computational vision, will be discussed.

The classes will focus on algorithmic and methodological aspects, while trying to give an idea of the underlying theoretical underpinnings. Practical laboratory sessions will give the opportunity to have hands-on experience.


Program

RegML is a 20 hours advanced machine learning course including theory classes and practical laboratory sessions. The course covers foundations as well as recent advances in Machine Learning with emphasis on high dimensional data and a core set techniques, namely regularization methods. In many respects the course is a compressed version of the 9.520 course at MIT.


  • Introduction to Statistical Machine Learning

  • Tikhonov Regularization and Kernels

  • Binary classification and model selection

  • Early Stopping and Spectral Regularization

  • Regularization for Multi-task Learning

  • Spectral filters and multi-class classification

  • Sparsity Based Regularization

  • Structured Sparsity

  • Sparsity-based learning

  • Data Representation


    Dictionary Learning

  • Data Representation


    Deep Learning

Registration fee

Once accepted, each candidate has to follow the instructions in the acceptance email and proceed with the payment. The registration fee is non-refundable.

Fees
students and postdocs: waived
professors: waived
professionals: EUR 150
UniGe students and IIT affiliates: no fee


Application

TBD


Organizers


References

MLCC 2020. A one week (crash) course of 10 lectures, including theoretical and practical sessions

MIT 9.520 Statistical Learning Theory and Applications. This is a term long course of roughly
25 lectures offered to graduate students at MIT

Machine Learning 2018/2019. Undergraduate term-long introductory Machine Learning course offered at the University of Genova

CBMM Summer School: Machine Learning Classes. One day introduction to the essential concepts and algorithms at the core of modern Machine Learning

RegML master page. Previous editions of RegML. The course started in 2008 has seen an increasing national and international attendance over the years, with a peak of over 90 participants in 2014