Probability (3 credits)

 

Instructor: Prof. Shou-de Lin (sdlin@csie.ntu.edu.tw) , Office 333

Classroom: CSIE 104

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

Office Hour:  Mon after class or by appointment

TA: fw, iγ͡Aʺ~

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.

Grading:
Midterm: (40% )
Final: (40%)
Project: (20%)

Textbook:

Probability and Statistical Inference (8th or 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

Syllabus:

23-Feb

Introduction

2-Mar

Axiom_prob, Conditional Prob, Independence, Baye's Rule

9-Mar

Random variables, mean and variance

16-Mar

discrete prob distribution

23-Mar

discrete prob distribution

30-Mar

Continuous Probability Distribution, Normal Distribution, Multivariable distributions,

6-Apr

Break

13-Apr

Conditional distributions, correlation, independency, distribution of functions

20-Apr

Midterm

27-Apr

Midterm Analysis

4-May

Chebyshev's inequality, Law of large number, Central Limit Theorem, Final Project Description

11-May

Final Project Proposal, Estimation, chi-square

18-May

Confidence Interval + Hypothesis Test

25-May

Information Theory

1-Jun

Language models & others

8-Jun

Probability & Life, final project presentation

15-Jun

Final Exam

22-Jun

Final Project Presentation/Demo