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:  Thur after class or by appointment

TA: Cayon Liow Keei Yann cayon.1318.96@hotmail.com, Li-Wei Chang r08922041@g.ntu.edu.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: (35% )
Final: (35%)
Project: (30%)

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:

 2る20ら Introduction 2る27ら Axiom_prob, Conditional Prob, Independence, Baye's Rule 3る5ら Random variables, mean and variance 3る12ら discrete prob distribution 3る19ら Continuous Probability Distribution, Normal Distribution 3る26ら Multivariable distributions 4る2ら break 4る9ら Midterm 4る16ら Conditional distributions, correlation, independency, distribution of functions 4る23ら Chebyshev's inequality, Central Limit Theorem, Law of large number 4る30ら Final Project Proposal 5る7ら Central Limit Theorem, estimation, chi-square 5る14ら Confidence Interval + Hypothesis Test 5る21ら Information Theory 5る28ら Final Project Presentation 1 6る4ら Language models & others, Probability & Life 6る11ら Final Exam 6る18ら Final Project Presentation 2