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Lectures are given every week on Monday 14:30-16:00 in KN:E-127.

Lect. | Topic |
---|---|

01 | Markov chains, equivalent representations, ergodicity, convergence theorem for homogeneous Markov chains |

02 | Hidden Markov Models on chains for speech recognition: pre-processing, dynamic time warping, HMM-s |

03 | Recognising the generating model – calculating the emission probability for a measured signal sequence. |

04 | Recognising the most probable sequence of hidden states and the sequence of most probable states. |

05 | Possible formulations for supervised and unsupervised learning tasks (parameter estimation). |

06 | Supervised and unsupervised learning according to the Maximum-Likelihood principle, the Expectation Maximisation algorithm. |

07 | Hidden Markov models on acyclic graphs (trees). Estimating the graph structure. |

08 | Hidden Markov models with continuous state spaces. Kalman filter and particle filters. |

09 | Markov Random Fields - Markov models on general graphs. Equivalence to Gibbs models, Examples from Computer Vision. |

10 | Relations to Constraint Satisfaction Problems and Energy Minimisation tasks, unified formulation, semi-rings. |

11 | Searching the most probable state configuration: transforming the task into a MinCut-problem for the submodular case. |

12 | Searching the most probable state configuration: approximative algorithms for the general case. |

13 | The partition function and marginal probabilities: Approximative algorithms for their estimation. |

14 | Duality between marginal probabilities and Gibbs potentials. The Expectation Maximisation algorithm for parameter learning. |

courses/ae4m33gmm/materials/lectures.txt · Last modified: 2013/10/04 13:02 (external edit)