The LIF organises the second bootcamp of the European Network of Excellence PASCAL (Pattern Analysis, Statistical modelling and ComputAtional Learning) . This school will be held during summer 2010, from July the 5th (afternoon) to July the 13th (morning), on the campus of St. Charles in Marseille.

The bootcamp is an international event which consists of a one week courses (lectures, pratical and lab sessions) designed for Master students or students just about to start a PhD. It aims at training good-level students from various scientific fieds to major thematics of PASCAL network, particularly machine learning.
The first PASCAL bootcamp was held in Barcelona during summer 2007.

Machine Learning

Machine Learning is a multidisciplinary field which involves theoretical works linked with language theory, database theory, combinatorial optimisation, artificial intelligence, information theory but also with inferential statistics or, more recently, signal processing. This discipline aims at extracting statistical regularities from a finite sample of observations in preparation from an inference process, where algorithmic, theoretical and practical aspects are major concerns.

Application fields of machine learning can be potentially extended to all problems where an automatic processing of data (numeric or structured) is required. It mainly includes data classification problems, document indexations, information retrieval, automatic text summarization, bio-informatics (such as protein structures prediction), automatic processing of natural language, information extraction from multimedia data, audio/video signal modelisation (source separation, sequence classification).
Most of these domains are upstream from data mining and web mining, two booming sectors of computer science with major economic issues.

Attendance and Educational Objective

Up to now, Machine Learning researchers get an inadequate academical formation. Indeed, most of european computer sciences Masters integrate only few machine learning modules, and are far from encompassing the global nature of our discipline. It is essential that PhD students (or students just about to start a PhD) get the opportunity to top up their formation with events such as this bootcamp.

This bootcamp is therefore designed for machine learning PhD students but also to PhD students from connate fields (statistics, bioinformatics, natural language processing, optimisation, signal processing...). Participants will receive a set of lessons on several major machine learning themes given by european experts from the PASCAL2 network.

Program Committee

  • John Shawe-Taylor, University College London
  • Colin de la Higuera, Université de Nantes
  • François Denis, Université de Provence
  • José Luis Balcázar, Universitat Politècnica de Catalunya
  • Alexis Nasr, Université de la Méditerranée
  • Rémi Eyraud, Université de Provence

Organisation Committee

The summercamp organisation is provided by the Laboratoire d'Informatique Fondamentale de Marseille (LIF). The committee is composed of the following members:

  • Stéphane Ayache, vice-president
  • Raphaël Bailly
  • Frédéric Bechet
  • François Denis
  • Cécile Capponi
  • Rémi Eyraud, president
  • Amaury Habrard
  • Pierre Machart
  • Alexis Nasr
  • Martine Quessada
  • Liva Ralaivola
  • Guillaume Stempfel

The school will focus on the role of statistical inference in biological modelling, with a particular emphasis on the Bayesian framework.


Cognitive science aims to reverse engineer human intelligence; machine learning provides one of our most powerful sources of insight into how machine intelligence is possible. Cognitive science therefore raises challenges for, and draws inspiration from, machine learning; and insights about the human mind may help inspire new directions for machine learning. This summer school brings together leading researchers from both fields, and those working at the interface between them. It is aimed at graduate students, post-docs and established researchers from both the cognitive science and machine learning communities, interested in exploring the interface between human and machine intelligence.

The machine learning team of the ”Labora-toire d’Informatique Fondamentale de Mar-seille” (ML-LIF) is a Joint Research Unit of the Universit´e de Provence and the CNRS.All of the 5  professors/assistant professors of the team are involved in the  organisation of aspring school in machine learning that will beheld at the end of next May. The particular-ity of this school, compared to the machinelearning summer schools usually supportedby PASCAL, is that it is targeted to an at-tendance made of people coming from variousscientific fields such as mere statistical infer-ence, bioinformatics, machine vision. Thisshort note summarizes some of the salientaspects regarding this school; it particularlystresses the diffusion of machine learning overa wide range of research areas.

A second item that is covered by this notedeals with the teaching experience of the ma-chine learning team members. It brings someclues as to how undergraduate and graduatestudents positively perceive machine learningand may fail to sustain their interest whenthe theoretical aspects of the field are tack-led.

The 13th Machine Learning Summer School will be held in Cambridge, UK. This year's edition is organized by the University of Cambridge, Microsoft Research and PASCAL. The school will offer an overview of basic and advanced topics in machine learning through theoretical and practical lectures given by leading researchers in the field. We hope to attract international students, young researchers and industry practitioners with a keen interest in machine learning and a strong mathematical background.

Lectures will be held at the Centre for Mathematical Sciences and at Microsoft Research Cambridge. All participants will be accomodated at Churchill College.

In addition to a broad-range of lectures on state-of-the-art Computer Vision techniques, it offers exciting sport activities, such as Kung-Fu, Ultimate Frisbee, and Volleyball.

The Summer Schools in Logic and Learning bring together two annual summer schools in the area of logic and machine learning: the Logic Summer School and the Machine Learning Summer School. The summer schools will be hosted by the Computer Sciences Laboratory in the Research School of Information Sciences and Engineering at The Australian National University, from the 26 January to 6 February 2009.

The Logic courses will consist of short courses on aspects of pure and applied logic. The Machine Learning courses will consist of short courses on the theory and practice of machine learning, which combine deep theory from areas as diverse as Statistics, Mathematics, Engineering, and Information Technology with many practical and relevant real life applications. The courses will be taught by experts from Australia and overseas. The summer schools this year will also include a special track on Artificial Intelligence (AI), which will feature courses on aspects of both logic and machine learning.

The Logic courses will be held at the Psychology G6 lecture theatre, and the Machine Learning and Artificial Intelligence courses will be held at the Physics lecture theatre at the ANU. In addition to the scheduled courses, time will be set aside each day for practical classes, discussions and software demonstrations

AERFAI Summer School 2008 organized by AERFAI, the Spanish Association for Pattern Recognition and Image Analysis, in collaboration with the Pattern Recognition and Speech Technology Group of the University of the Basque Country and the Pattern Recognition and Human Language Technology of the Technical University of Valencia.
The aim of this Summer School is to introduce PhD students and young researchers into challenging areas of Natural Language Processing (NLP). The Summer School will consist of lectures by leading experts in the field along with experimental practices. The goal is to provide a forum for researches to interact and get to know the cutting-edge tools in Natural Language Processing and Computational Linguistics. We wish to emphasize that this is not a lecture-series covering the fundamentals of NLP but a get-together where latest ideas on NLP can be exchanged.

Machine Learning is a foundational discipline of the Information Sciences. It combines deep theory from areas as diverse as Statistics, Mathematics, Engineering, and Information Technology with many practical and relevant real life applications. The aim of the summer school is to cover the entire spectrum from theory to practice. It is mainly targeted at research students, IT professionals, and academics from all over the world.

The fourth Machine Learning Summer School was held in Berder Island, France between the 12th and the 25th of September, 2004. More than 100 students and researchers from 20 countries interested in Machine Learning attended. This years' summer school presented some of the topics which are at the core of modern Learning Theory. 15 distinguished authorities from the field gave 14 courses in slots from 4 to 8 hours. In addition, many evening talks which were focused on additional topics were presented. There were also three practical sessions organized providing a 'hands-on' experience of working with Machine Learning algorithms.