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

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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:

 23-Feb Introduction 2-Mar Axiom_prob, Conditional Prob, Independence, Baye's Rule 9-Mar Random variables, mean and variance 16-Mar discrete prob distribution 23-Mar discrete prob distribution 30-Mar Continuous Probability Distribution, Normal Distribution, Multivariable distributions, 6-Apr Break 13-Apr Conditional distributions, correlation, independency, distribution of functions 20-Apr Midterm 27-Apr Midterm Analysis 4-May Chebyshev's inequality, Law of large number, Central Limit Theorem, Final Project Description 11-May Final Project Proposal, Estimation, chi-square 18-May Confidence Interval + Hypothesis Test 25-May Information Theory 1-Jun Language models & others 8-Jun Probability & Life, final project presentation 15-Jun Final Exam 22-Jun Final Project Presentation/Demo