Probability (3 credits)

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

Classroom: CSIE 102

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

Office Hour:  Mon after class or by appointment

TA: 游斯涵：b00902003@ntu.edu.tw ；林善偉：b99902023@ntu.edu.tw李威承lightlighting@hotmail.com.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: (30%)
Final: (40%)
Homeworks: (30%)

Textbook:

Probability and Statistical Inference (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:

 Basic Probability Theory homeworks 26-Feb Introduction 5-Mar Axiom_prob, Conditional Prob, Independence, Baye's Rule hw1 12-Mar Random variables, mean and variance 19-Mar discrete prob distribution hw2 26-Mar discrete prob distribution hw3 2-Apr berak 9-Apr Continuous Probability Distribution, Normal Distribution hw4 16-Apr Multivariable distributions 23-Apr midterm 30-Apr Conditional distributions, correlation, independency, distribution of functions hw5 7-May Chebyshev's inequality, Central Limit Theorem, Law of large number 14-May Confidence Interval hw6 21-May Point Estimation & Chi-square fit hw7 28-May break 4-Jun Information Theory hw8 11-Jun Prob for Data Mining and Machine Learning 18-Jun Prob for Search and Social Networks 25-Jun Final