DATE |
TITLE |
Speaker |

11/1(¿ù) |
A Tutorial on Learning with Bayesian Networks |
K. Hwang |

11/3(¼ö) |
Introduction to Inference for Bayesian Networks |
S. Kim |

11/8(¿ù) |
An Introduction to Variational Methods for Graphical Models |
S. Park |

11/10(¼ö) |
Improving the Mean Field Approximation Via the Use of Mixture Distributions |
J. Lee |

11/10(¼ö) |
Introduction to Monte Carlo Methods |
J. Oh |

11/15(¿ù) |
Suppressing Random Walks in Markov Chain Monte Carlo using Ordered Overrelaxation |
J. Chang |

11/15(¿ù) |
A View of the EM Algorithm that Justifies Incremental, Sparse, and Other Variations |
K. Hwang |

11/17(¼ö) |
Latent Variable Models |
J. Oh |

11/17(¼ö) |
Stochastic Algorithms for Exploratory Data Analysis: Data Clustering and Data Visualization |
J. Lee |

11/22(¿ù) |
A Hierarchical Community of Experts |
J. Chang |

11/24(¼ö) |
A Mean Field Learning Algorithm for Unsupervised Neural Networks |
S. Kim |

11/24(¼ö) |
Prediction with Gaussian Processes: From Linear Regression to Linear Prediction and Beyond |
S. Park |