Probability (3 credits)


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

Classroom: CSIE 104

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

Office Hour:  Mon after class or by appointment


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