Literature

  • Wikipedia genetic algorithms
  • Karl Sims (1994) Evolving Virtual Creatures
  • Sutton/Barto: Reinforcement Learning: An Introduction, MIT Press,free web version
  • Andrieu et al. (2003) An Introduction to MCMC for Machine Learning
  • Neal (1993) Probabilistic Inference Using Markov Chain Monte Carlo Methods
  • Kemp, Perfors and Tenenbaum (2007) Learning overhypotheses with hierarchical Bayesian models
  • Video lectures, Cognitive Science and Machine Learning Summer School 2010 - Sardinia
  • Griffiths, T. L., & Tenenbaum, J. B. (2006). Optimal predictions in everyday cognition. Psychological Science, 17, 767-773
  • Vul, E & Pashler, H (2008) "Measuring the Crowd Within: Probabilistic representations Within individuals" Psychological Science. 19(7) 645-647
  • Vul, E., Goodman, N.D., Griffiths, T.L. & Tenenbaum, J.B. (2009) "One and Done? Optimal decisions from very few samples." 31st Annual Meeting of the Cognitive Science Society, 2009.
  • Denison, S., Bonawitz, E. B., Gopnik, A., & Griffiths, T. L. (in press). Preschoolers sample from probability distributions. Proceedings of the 32nd Annual Conference of the Cognitive Science Society.
  • S.L. Gershman, Y. Niv, Learning latent structure: carving nature at its joints. Current Opinion in Neurobiology, 2010
  • D.A. Braun, C. Mehring, D.M. Wolpert, Structure learning in action, Behavioural brain research, 2010
  • A Aertsen, DM Wolpert, C Mehring, Motor task variation induces structural learning, Current Biology, 2009
  • Marc Toussaint
  • Teaching Resources of Marc Toussaint
  • Lecture 2 in Lissabon (2009) Marc Toussaint