TML workshop 2008

Laboratoire Hubert Curien, May 6-7, 2008

Program

• Tuesday 6th

  • Session 1: Who machine learning should be taught to (9:00 - 10:30)

    • 09:00 - 09:30 Machine Learning in Secondary Education - Ricard Gavaldà, Barcelona, Spain (paper, talk)
    • 09:30 - 10:00 Some Hints on the Teaching of Machine Learning to Industrial Practitioners - Christopher Kermorvant, Paris, France (paper, talk)
    • 10:00 - 10:30 Master's Level Machine Learning: Public Perceptions - David Barber, London, United Kingdom (paper, talk)
  • Coffee break

  • Session 2: Some Master's with strong machine learning components (11:00 - 12:00)

    • 11:00 - 11:30 Interactions between the Industrial and Academic Skill for Teaching Machine Learning in Master's Programs - Younès Benanni and Céline Rouveirol, Paris, France (paper, talk)
    • 11:30 - 12:00 Macadamia: Master's Programme in Machine Learning and Data Mining - Tapani Raiko, Kai Puolamäki, Samuel Kaski, Jaakko Hollmén, Antti Honkela, Juha Karhunen, Heikki Mannila, Erkki Oja and Olli Simula, Helsinki, Finland (paper, talk)

  • Lunch

  • Session 3: Erasmus Mundus issues (14:00 - 15:00)

    • 14:00 - 14:30 About the Erasmus Mundus programme - Thomas Guillobez, Saint-Étienne, France (paper, talk)
    • 14:30 - 15:00 The Master Erasmus Mundus CIMET "Color in Informatics and Media Technology" - Alain Trémeau, Saint-Etienne, France (paper, talk)
  • Bus break

  • Session 4: What should be taught

    • Theoritical Computer Science for Machine Learning - Colin de la Higuera, Saint-Étienne, France (paper, talk)
    • Teaching Experiments and Programming for Machine Learning - François Jacquenet, Saint-Étienne, France (paper, talk)
    • What is the place of Machine Learning between Pattern Recognition and Optimization? - Laurent Miclet, Lannion and Antoine Cornuéjols, Paris, France (paper, talk)

  • Debate

  • Banquet

• Wednesday 7th

  • Session 5: How machine learning might be taught (9:00 - 10:00)

    • 09:00 - 09:30 Learning mixture models - courseware for finite mixtures of Bernoulli distributions - Jaakko Hollmén and Tapani Raiko, Helsinki, Finland (paper, talk)
    • 09:30 - 10:00 Triad "Lectures-Labs-Contest" as a form of Research Based Approach in Teaching Machine Learning - Igor Chikalov Victor Eruhimov, Intel, Alexey Polovinkin and Nikolai Yu. Zolotykh, Nizhni Novgorod, Russia (paper)
  • Coffee break

  • Session 6: Teaching a larger audience (10:30 - 12:00)

    • 10:30 - 11:00 A point of view on teaching Pattern Recognition - Maria Luisa Micó, Alicante, Spain (paper, talk)
    • 11:00 - 11:30 Spring School in Machine Learning Teaching experiences - Cécile Capponi, François Denis, Rémi Eyraud, Amaury Habrard and Liva Ralaivola, Marseille, France (paper, talk)
    • 11:30 - 12:00 Discussion

  • Lunch

  • End of workshop