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


Course Description:

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.

Midterm 1: (30%)
Midterm 2: (30%)
Final: (30%)
Participation: (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
25-Feb Introduction
4-Mar Axiom_prob, Conditional Prob, Independence, Bayesian
11-Mar Random variables, mean and variance
18-Mar discrete prob distribution
25-Mar continuous prob distribution
1-Apr Midterm 1
Advanced Probability Theory
8-Apr Normal Distribution & Central Limit Theorem
15-Apr Multivariable distribution
22-Apr Chebyshev, Law of large numbers
29-Apr Confidence Interval and Hypothesis Testing
6-May Bayesian Estimation
13-May Midterm 2
Other related Topics & Applications
20-May Point Estimation & Chi-square fit
27-May Information Theory
3-Jun Prob for Data Mining and Social Networks
10-Jun Prob for Search and NLP
17-Jun Probability & Life
24-Jun Final