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.
Grading:
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 |
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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 | ¡@ |