2010:ml

The study of learning from data is commercially and scientifically important. This one month short course is designed to give first year Ph.D. students a thorough grounding in the methodologies, technologies, mathematics and algorithms currently needed by people who do research in learning and data mining or who may need to apply learning or data mining techniques to a target problem. The topics of the course draw from classical statistics, from machine learning, from data mining, from Bayesian statistics and from statistical algorithmics.

Students entering the class should have a pre-existing working knowledge of probability, statistics and algorithms, though the class has been designed to allow students with a strong numerate background to catch up and fully participate.

Topic | Date | Slides | note |
---|---|---|---|

Introduction | 2010.03.04 | Introduction | => |

What is Machine Learning | |||

Point estimation | |||

Supervised Learning | 2010.03.11 | Concept learning | => |

Decision tree | |||

Naive Bayes classifier | |||

Support Vector Machines | |||

Boosting | |||

Unsupervised Learning | 2010.03.18 | Dimension Reduction | => |

Why AddBoost? | |||

Clustering | |||

Reinforcement Learning | 2010.03.25 | Clustering (cont.) | => |

Hiden Markov Model | |||

Kalman Filter | |||

course talk | 2010.04.01 | Robust PCA | By Zhengfang Hu |

2010/ml.txt · Last modified: 2014/05/22 08:34 (external edit)

Except where otherwise noted, content on this wiki is licensed under the following license: CC Attribution-Noncommercial-Share Alike 4.0 International