Probability (3 credits)

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

Classroom: CSIE 104

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

Office Hour:  Mon after class or by appointment

TA: TBA

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 1: (30%)
Midterm 2: (30%)
Final: (30%)
Homework Assignments: (10%)

Textbook:

Probability and Statistical Inference (8th 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 (tentative):

 Basic Probability Theory 20-Feb Introduction 5-Mar Axiom_prob, Conditional Prob, Independence, Baye's Rule 12-Mar Random variables, mean and variance 19-Mar discrete prob distribution 26-Mar continuous prob distribution 2-Apr Midterm 1 Advanced Probability Theory 9-Apr Normal Distribution, Multivariable distributions 16-Apr Conditional distributions, correlation, independency 23-Apr Central Limit Theorem, Law of large number 30-Apr Chebyshev's inequality 7-May Midterm 2 Other related Topics & Applications 14-May Confidence Interval 21-May Point Estimation & Chi-square fit 28-May Information Theory 4-Jun Prob for Data Mining and Machine Learning 11-Jun Prob for Search and Social Networks 18-Jun Final