Probability (3 credits)


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

Classroom: CSIE 102

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

Office Hour:  Mon after class or by appointment

TA: 游斯涵 ;林善偉李威承

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: (30%)
Final: (40%)
Homeworks: (30%)


Probability and Statistical Inference (9th 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


Basic Probability Theory homeworks
26-Feb Introduction  
5-Mar Axiom_prob, Conditional Prob, Independence, Baye's Rule hw1
12-Mar Random variables, mean and variance  
19-Mar discrete prob distribution hw2
26-Mar discrete prob distribution hw3
2-Apr berak  
9-Apr Continuous Probability Distribution, Normal Distribution hw4
16-Apr Multivariable distributions  
23-Apr midterm  
30-Apr Conditional distributions, correlation, independency, distribution of functions hw5
7-May Chebyshev's inequality, Central Limit Theorem, Law of large number  
14-May Confidence Interval hw6
21-May Point Estimation & Chi-square fit hw7
28-May break  
4-Jun Information Theory hw8
11-Jun Prob for Data Mining and Machine Learning  
18-Jun Prob for Search and Social Networks  
25-Jun Final