Probability (3 credits)

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

Classroom: CSIE 103

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

Office Hour:  Thursday after class or by appointment

TA: Chun-Chao Yen (r96944016@csie.ntu.edu.tw), Tzu-Kuo Huang (intellab@csie.ntu.edu.tw), Hung-Yi Lo (hungyi@iis.sinica.edu.tw), Chien-Lin Tseng (gagedark@gmail.com )

Course Description:

This goal of 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. We will talk about statistic inference and estimation methods in the second part of this course. Finally 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.

Class Participation (10%)
Assignments: (20%)
Midterm: (35%)
Final: (35%)

Textbook:

Probability and Statistical Inference (Hogg & Tanis)

Reference books:

Introduction to Bayesian Statistics (1st or 2nd edition), William Bolstad
Data Analysis - a Bayesian tutorial (2nd edition) D.S. Sivia
Probability and Statistics for Computer Science, James L. Johnson

Syllabus (tentative):

 Date Topic Notes Probability Theory Feb 21 Introduction ¡@ Feb 28 Holiday ¡@ March 6 Basics in Probability ¡@ March 13 Discrete, Continuous, and Multivariate Distribution ¡@ March 20 Discrete, Continuous, and Multivariate Distribution ¡@ March 27 Discrete, Continuous, and Multivariate Distribution ¡@ April 3 Discrete, Continuous, and Multivariate Distribution ¡@ April 10 Simulation, Law of large numbers, Central Limit Theorem ¡@ April 17 Midterm ¡@ Statistic Inference and Estimation Theory April 24 Estimation ¡@ May 1 Statistical Hypothesis Test ¡@ May 8 Bayesian Inference ¡@ May 15 Bayesian Networks ¡@ Applications May 22 Information Theory ¡@ May 29 Probability for Information Retrieval ¡@ Jun 5 Probability for Machine Learning ¡@ Jun 12 Probability for Data Mining ¡@ Jun 19 Final Exam ¡@