Probability (3 credits)


Instructor: Prof. Shou-de Lin ( , Office 333

Classroom: CSIE 102

Meeting Time: Thursday 14:20-17:20 pm

Office Hour:  Thursday after class or by appointment


Course Description (you can download the first lecture here):

This goal of course is to equip students with sufficient background knowledge to perform probabilistic and statistical analysis on CS-related problems.  In the first part of this course, fundamental knowledge about probability theory will be discussed.  In the second part, we will demonstrate how the concept of probability and statistics can be applied to deal with real-world computer science problems including search engine, machine learning, data mining, and natural language processing.


Homework assignments: (20%)
Midterm: (40%)
Final: (40%)


Probability and Statistical Inference (Hogg & Tanis)

Reference books:

Introduction to Bayesian Statistics (1st or 2nd edition), William Bolstad
Probability and Statistics for Engineering and Science, Jay Devore
Probability and Statistics for Computer Science, James L. Johnson

Syllabus (tentative):

Fundamental About Probability Theory
19-Feb Introduction
26-Feb Axiom_prob, Conditional Prob, Independence, Bayesian
5-Mar Random variables, mean and variance
12-Mar discrete prob distribution
19-Mar continuous prob distribution
26-Mar Multivariable distribution
2-Apr Holiday
9-Apr distribution of functions
16-Apr Midterm
Advanced Probability Theory
23-Apr Chebyshev, Law of large numbers, central limit theorem
30-Apr Estimation Theory
7-May Information Theory
14-May Bayesian Networks
21-May Prob. For Data Mining and social network Analysis
28-May Holiday
4-Jun Prob. for Search and Natural langauge processing 
11-Jun Probability and Life
18-Jun Final