Probability (3 credits)


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

Classroom: CSIE 104

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

Office Hour:  Thursday after class or by appointment

TA: Yu-han Chen (, Chin-Hua Tsai ( ), Sirius Chen (

Course Description:

The goal of this 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. Then we will move into some advanced techniques about probability theory.  In the final 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.

Midterm 1: (30%)
Midterm 2: (30%)
Final: (30%)
Homework Assignments: (10%)


Probability and Statistical Inference (8th version, Hogg & Tanis)

Reference books:

Probability for Electrical and Computer Engineers, Charles Therrien, Murali Tummala
Probability and Statistics for Engineering and Science, Jay Devore
Probability and Statistics for Computer Science, James L. Johnson

Syllabus (tentative):

Basic Probability Theory
24-Feb Introduction
3-Mar Axiom_prob, Conditional Prob, Independence, Baye's Rule
10-Mar Random variables, mean and variance
17-Mar discrete prob distribution
24-Mar continuous prob distribution
31-Mar Midterm 1
Advanced Probability Theory
7-Apr Normal Distribution, Multivariable distributions
14-Apr Conditional distributions, correlation, independency
21-Apr Central Limit Theorem, Law of large number
28-Apr  Chebyshev's inequality
5-May Confidence Interval
12-May Midterm 2
Other related Topics & Applications
19-May Point Estimation & Chi-square fit
26-May Information Theory
2-Jun Prob for Data Mining and Machine Learning
9-Jun Prob for Search and Social Networks
16-Jun Probability & Life
23-Jun Final