Probability (3 credits)


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

Classroom: CSIE 104

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

Office Hour:  Mon after class or by appointment

TA: ©ӡ] ^AŦ]^Afw]^

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


1-Mar Introduction
8-Mar Axiom_prob, Conditional Prob, Independence, Baye's Rule
15-Mar Random variables, mean and variance
22-Mar discrete prob distribution
29-Mar discrete prob distribution
5-Apr break
12-Apr Continuous Probability Distribution, Normal Distribution
19-Apr Multivariable distributions
26-Apr Midterm
3-May Conditional distributions, correlation, independency, distribution of functions
10-May Chebyshev's inequality, Central Limit Theorem, Law of large number
17-May Final Project Proposal
24-May Central Limit Theorem, estimation, chi-square
31-May Confidence Interval + Hypothesis Test
7-Jun Information Theory
14-Jun Language models & others, Probability & Life
21-Jun Final Project Presentation/Demo
28-Jun Final Exam