Statistical Methods for Intelligent Information Processing (3 credits)

Instructor: Prof. Shou-de Lin (sdlin@csie.ntu.edu.tw) , Office 333

Classroom: CSIE 111

Meeting Time: Tue 14:20-17:20 pm

Office Hour: After class or by appointment

TA: TBA

Course Description:

This course teaches how to process
information intelligently using statistical methods and algorithms.

**Grading:
**

Final Project: (40%)

**
Reference books:**

Elements of Statistical Learning by Trevor Hastie, Robert Tibshirani and
Jerome Friedman (ISBN 0387952845)

Pattern Recognition and Machine Learning by Chris Bishop (SBN 0387310738)

Machine Learning by Tom Mitchell (ISBN 0070428077)

The EM algorithm and related statistical models / edited by Michiko
Watanabe, Kazunori Yamaguchi

Reinforcement learning : an introduction / Richard S. Sutton and Andrew G.
Barto MIT Press, c1998

**Syllabus (tentative):**

16-Sep | introduction+Basic |

Supervised Learning | |

23-Sep | Regression, DT, ME |

30-Sep | VC dimension, SVM, Lazy Learning |

7-Oct | HMM, , Bayesian |

14-Oct | Imbalanced Data Classification |

Unsupervised Learning | |

21-Oct | LM+viterbi |

28-Oct | EM |

4-Nov | EM+clustering |

11-Nov | Labelling |

Reinfocement learning | |

18-Nov | Monte Carlo, MDP |

25-Nov | Q-learning |

2-Dec | Project Proposal |

9-Dec | SARSA |

Machine Discovery | |

16-Dec | Advanced LM |

23-Dec | Discovery in Social Network |

30-Dec | Advanced topics in KDD |

6-Jan | Final Project Presentation |

13-Jan | Final Project Presentation |

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