Probability (3 credits)


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

Classroom: CSIE 104

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

Office Hour:  Thur 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: (40% )
Final: (40%)
Project: (20%)


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


2る21ら Introduction
2る28ら no class
3る7ら Axiom_prob, Conditional Prob, Independence, Baye's Rule
3る14ら Random variables, mean and variance
3る21ら discrete prob distribution
3る28ら Continuous Probability Distribution, Normal Distribution
4る4ら break
4る11ら Multivariable distributions
4る18ら Midterm
4る25ら Conditional distributions, correlation, independency, distribution of functions
5る2ら Chebyshev's inequality, Central Limit Theorem, Law of large number
5る9ら Final Project Proposal
5る16ら Central Limit Theorem, estimation, chi-square
5る23ら Confidence Interval + Hypothesis Test
5る30ら Information Theory
6る6ら Language models & others, Probability & Life
6る13ら Final Project Presentation/Demo
6る20ら Final Exam