#### Prerequisites & Bulletin Description

#### Course Objectives

Upon completion of this course a student should be able to

- Obtain a strong conceptual understanding of statistical learning.
- Learn the statistical principles behind many of the approaches to supervised & unsupervised learning.
- Understand how to perform model selection & evaluation and to effectively communicate the results.
- Learn how to rigorously analyze data using modern statistical methods and computer software.
- Obtain hands-on experience by analyzing real data sets with the skills learned throughout the course.

#### Evaluation of Students

Students will be graded on written homework assignments, data analysis projects, midterm and final examinations.

#### Course Outline

The following timeline is approximate.

Topics |
Number of Weeks |

Introduction to statistical leaning | 1 week |

Linear regression | 1 week |

Classifications | 3 weeks |

Methods for model evaluation, model selection and regularization | 3 weeks |

Nonparametric approaches: nearest neighbors, splines, generalized additive models and support vector machine | 3 weeks |

Ensemble methods: bagging, boosting and random forests | 2 weeks |

Unsupervised learning: dimensionality reduction and clustering | 2 weeks |

#### Textbooks & Software

*An Introduction to Statistical Learning, with applications in R* (2013) by G. James, D. Witten, T. Hastie, R. Tibshirani.

*R* by the R Development Core Team.

Submitted by: Tao He

Date: May 2, 2016