National Tsing Hua University
Institute of Information Systems & Applications

Course No.: ISA6320

Course Title: Advanced Probability and Statistics      

Credits: 3 (Spring, 2012)

Classes: 11:10-12 Wednesday, 10:10-12 Friday at EECS 127

Instructor: Dr. Chaur-Chin Chen, http://www.cs.nthu.edu.tw/~cchen

Tel/E-mail: (03) 573-1078, cchen@cs.nthu.edu.tw, (03) 571-5131 Ext. 33557 (T.A.s)

Prerequisites: Calculus (I, II), Data Structure, C/C++ or Java, [Linear Algebra, Matlab]

Contents:
0. Course Description       Review of Calculus   
1. Introduction
2. Descriptive Statictics     Table 2.1 Starting Salary     Bar and Curve Code     Bar and Curve Plots     Pie Code     Pie Chart     Table 2.8 Temperature and Defect Data     Scatter Code     Scatter Plot    
3. Elements of Probability       Elements of Probability
4. Random Variables and Expectations       Random Variables and Expectations
5. Special Random Variables       Special Random Variables     Shapes of Distributions     Matlab Codes     Student-t and F Distributions     Matlab Codes    
6. Distributions of Sampling Statistics       Distributions of Sampling Statistics
7. Parameter Estimation       Parameter Estimation
8. Hypothesis Testing       Hypothesis Testing
9. Regression       Regression
10. Analysis of Variance       Analysis of Variance
11. Goodness of Fit Tests and Categorical Data Analysis       Goodness of Fit Tests and Categorical Data Analysis
12. Nonparametric Hypothesis Tests       Nonparametric Hypothesis Tests

Textbooks:
1. S.M. Ross Introduction to Probability and Statistics for Engineers and Scientists (v.4, 2009)
2. Online Lecture Notes
References:
1. C.M. Bishop, Pattern Recognition and Machine Learning (2006)
2. J.A. Gubner, Probability and Random Processes for Electrical and Computer Engineers, Cambridge Press (2006)
3. D. Hanselman and B. Littlefield, Mastering MatLab 7 (2005)
4. R.V. Hogg and E.A. Tanis, Probability and Statistical Inference, Prentice-Hall (v7, 2006+)
5. G. Recktenwald, Numerical Methods with Matlab (2000)
6. S. Theodoridis and K. Koutroumbas, Pattern Recognition, Academic Press, (v4, 2009+)

Grading:
   
(40%) Assignments and Class Attendance
Assignment 1      
Assignment 2      
Assignment 3      
(30%) Tests
Exam 1     Solutions for Exam 1    
Exam 2 (Oral Presentation on Regression)    
(30%) Presentation and Report
no more than 16 pages is submitted in an e-mail attachment     Sample     No later than 11:59 am Tuesday, June 12, 2012    
Updated on June 9, 2012