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

TA: Cayon Liow Keei Yann, Li-Wei Chang

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: (35% )
Final: (35%)
Project: (30%)


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20ら Introduction
2る27ら Axiom_prob, Conditional Prob, Independence, Baye's Rule
3る5ら Random variables, mean and variance
3る12ら discrete prob distribution
3る19ら Continuous Probability Distribution, Normal Distribution
3る26ら Multivariable distributions
4る2ら break
4る9ら Midterm
4る16ら Conditional distributions, correlation, independency, distribution of functions
4る23ら Chebyshev's inequality, Central Limit Theorem, Law of large number
4る30ら Final Project Proposal
5る7ら Central Limit Theorem, estimation, chi-square
5る14ら Confidence Interval + Hypothesis Test
5る21ら Information Theory
5る28ら Final Project Presentation 1
6る4ら Language models & others, Probability & Life
6る11ら Final Exam
6る18ら Final Project Presentation 2