An Introduction and Some Developments in Statistics and Data Mining

  • 申立勇
  • Created: 2014-12-08
An Introduction and Some Developments in Statistics and Data Mining

 

Course No.2804Z    

Period10     

Credits0.5    

Course CategoryLecture     

Primary Coverage:
Course Description: The subject of this course is closely related to pattern recognition and machine learning. This course will introduce various statistical techniques for extracting useful information (i.e. learning) from data. Topics to be covered include a subset of linear discriminant analysis, tree-structured classifiers, feed-forward neural networks, support vector machines, other nonparametric methods, classifier ensembles (such as bagging and boosting), and unsupervised learning. These techniques can be applied in many fields, such as in marketing and bioinformatics/computational biology.
For whom intended: This course is designed for graduate students in statistics, computer science, engineering, business and biology who have relevant statistical background.
Prerequisites: A course in statistics or permission of instructor, and some programming background in using a higher level language, such as FORTRAN,C/C++, JAVA and Splus/R.
Objective: After taking the course, the student should have a working knowledge of using various machine learning techniques in practice.
Methods of Instruction and Work Expectations: In-class lectures are the main method of instruction. Students are expected to come to class and participate in discussions.
Textbooks:
Hastie T, Tibshirani R and Friedman J (2001). The Elements of Statistical Learning, Data Mining, Inference and Prediction. Springer.
A good reference is
Ripley BD(1996). Pattern Recognition and Neural Networks. Cambridge Univeristy Press.
Possible Topics:
1.Introduction
2.Linear regression, logistic regression, linear discriminate analysis, and flexible discriminant analysis
3.Nonparametric methods: nearest neighbor, mixture models
4.Tree-structured classifiers
5.Neural networks
6.Support vector machines
7.Bagging and boosting
8.Unsupervised learning

 

                                              AuthorWei Pan