Probability (3 credits)
Instructor: Prof. Shoude Lin (sdlin@csie.ntu.edu.tw) , Office 333
Classroom: CSIE 104
Meeting Time: Mon 14:2017:20 pm
Office Hour: Mon after class or by appointment
TA: TBA
Course Description:
The goal of this course is to equip students with
sufficient background knowledge to perform probabilistic and statistical
analysis on CSrelated 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 realworld computer science problems
including search engine, machine learning, data mining, and natural language
processing.
Grading:
Midterm 1: (30%)
Midterm 2: (30%)
Final: (30%)
Homework Assignments: (10%)
Textbook:
Probability and Statistical Inference (8th 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 (tentative):
Basic Probability Theory  
18Feb  Introduction  
25Feb  Axiom_prob, Conditional Prob, Independence, Baye's Rule  
4Mar  Random variables, mean and variance  
11Mar  discrete prob distribution  
18Mar  continuous prob distribution  
25Mar  Midterm 1  
Advanced Probability Theory  
1Apr  Normal Distribution, Multivariable distributions  
8Apr  Conditional distributions, correlation, independency  
22Apr  Central Limit Theorem, Law of large number  
29Apr  Chebyshev's inequality  
6May  Midterm 2  
Other related Topics & Applications  
13May  Confidence Interval  
20May  Point Estimation & Chisquare fit  
27May  Information Theory  
3Jun  Prob for Data Mining and Machine Learning  
10Jun  Prob for Search and Social Networks  

Final 