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: 李威承（b01902065@ntu.edu.tw ），陳宣佑（d06944005@ntu.edu.tw），王柏安（poanwang@iis.sinica.edu.tw

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%)

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:

 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