Probability (3 credits)
Instructor: Prof. Shou-de Lin (sdlin@csie.ntu.edu.tw) , Office 333
Classroom: CSIE 104
Meeting Time: Thursday 14:20-17:20 pm
Office Hour: Thursday after class or by appointment
TA: TBA
Course Description:
This goal of 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.
In the second 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%)
Participation: (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 | |
| 25-Feb | Introduction |
| 4-Mar | Axiom_prob, Conditional Prob, Independence, Bayesian |
| 11-Mar | Random variables, mean and variance |
| 18-Mar | discrete prob distribution |
| 25-Mar | continuous prob distribution |
| 1-Apr | Midterm 1 |
| Advanced Probability Theory | |
| 8-Apr | Normal Distribution & Central Limit Theorem |
| 15-Apr | Multivariable distribution |
| 22-Apr | Chebyshev, Law of large numbers |
| 29-Apr | Confidence Interval and Hypothesis Testing |
| 6-May | Bayesian Estimation |
| 13-May | Midterm 2 |
| Other related Topics & Applications | |
| 20-May | Point Estimation & Chi-square fit |
| 27-May | Information Theory |
| 3-Jun | Prob for Data Mining and Social Networks |
| 10-Jun | Prob for Search and NLP |
| 17-Jun | Probability & Life |
| 24-Jun | Final |