Class Times: |
Mondays, 14:00--17:00 |

Location: |
Roberts 4.21 |

Instructor (first half): |
Mark Herbster Office: 8.03, CS Building, Malet Place |

Instructor (second half): |
Massimiliano Pontil Office: 8.08, CS Building, Malet Place |

- T. Hastie, R. Tibshirani and J. Friedman.
**The Elements of Statistical Learning: Data Mining, Inference, and Prediction.**Springer, 2002. - C.M. Bishop.
**Pattern Recognition and Machine Learning**Springer, 2006. - N. Cristianini and J. Shawe-Taylor.
**An Introduction to Support Vector Machines**Cambridge University Press, 2001. - R.O. Duda, P.E. Hart and D.G. Stork.
**Pattern Classification.**Wiley, 2nd edition, 2004. - D.J.C. MacKay.
**Information Theory, Pattern Recognition and Neural Networks.**Cambridge Press, 2003 - T. Mitchell.
**Machine Learning.**McGraw Hill, 1997 - J. Shawe-Taylor and N. Cristianini.
**Kernel Methods for Pattern Analysis.**Cambridge University Press, 2004. - B.Scholkopf and A.J. Smola.
**Learning with Kernels**. MIT Press, 2002. - V.N. Vapnik.
**Statistical Learning Theory**. Wiley, New York, 1998.

Date | Title |
---|---|

Monday, September 29 | Introduction to Supervised Learning |

Monday, October 6 | Statistical Learning Theory (DRAFT) |

Monday, October 13 | Kernels and Regularisation (DRAFT) |

Monday, October 20 | Lab: Applying Regresion |

Monday, October 27 | Support vector machines (DRAFT) |

Monday, November 3 | No lectures (reading week) |

Monday, November 10 | Graph-based Semi-Supervised Learning |

Monday, November 17 | Proximal Methods (DRAFT) |

Monday, November 24 | Multi-task Learning (DRAFT) |

Monday, December 1 | Sparsity-based Methods |

Monday, December 8 | Online Learning (DRAFT) |

**Other suggested references:**