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:  Meng-Ru Wu (ray7102ray7102@gmail.com), Yu-Hsiang Huang (b07502159@csie.ntu.edu.tw), Ting-Wei WU (b07902102@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.

Grading:
Midterm: (40% )
Final: (50%)
Homework & Participation: (10%)
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月17日 Introduction HW
2月24日 Axiom_prob, Conditional Prob, Independence, Baye's Rule 　
3月3日 Random variables, mean and variance HW1
3月10日 discrete prob distribution 　
3月17日 Continuous Probability Distribution, Normal Distribution HW2
3月24日 distribution of functions,  Normal Distribution, Multivariate Distribution 　
3月31日 Conditional distributions, correlation, independency HW3
4月7日 Midterm Review 　
4月14日 Midterm 　
4月21日 Chebyshev's inequality, Central Limit Theorem, Law of large number 　
4月28日 Midterm Analysis 　
5月5日 Estimation, chi-square HW4
5月12日 Confidence Interval + Hypothesis Test 　
5月19日 Information Theory, Language Models HW5
5月26日 Probability & Life 　
6月2日 Final 　
6月9日 Final Analysis