Probability (3 credits)
Instructor: Prof. Shou-de Lin (sdlin@csie.ntu.edu.tw) , Office 333
Classroom: CSIE 104
Meeting Time: Mon 14:20-17:20 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 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 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 | |
| 20-Feb | Introduction |
| 5-Mar | Axiom_prob, Conditional Prob, Independence, Baye's Rule |
| 12-Mar | Random variables, mean and variance |
| 19-Mar | discrete prob distribution |
| 26-Mar | continuous prob distribution |
| 2-Apr | Midterm 1 |
| Advanced Probability Theory | |
| 9-Apr | Normal Distribution, Multivariable distributions |
| 16-Apr | Conditional distributions, correlation, independency |
| 23-Apr | Central Limit Theorem, Law of large number |
| 30-Apr | Chebyshev's inequality |
| 7-May | Midterm 2 |
| Other related Topics & Applications | |
| 14-May | Confidence Interval |
| 21-May | Point Estimation & Chi-square fit |
| 28-May | Information Theory |
| 4-Jun | Prob for Data Mining and Machine Learning |
| 11-Jun | Prob for Search and Social Networks |
| 18-Jun | Final |