Probability (3 credits)
Instructor: Prof. Shou-de Lin (sdlin@csie.ntu.edu.tw) , Office 333
Classroom: CSIE 102
Meeting Time: Thursday 14:20-17:20 pm
Office Hour: Thursday after class or by appointment
TA: TBA
Course Description (you can download the first lecture here):
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:
Homework assignments: (20%)
Midterm: (40%)
Final: (40%)
Textbook:
Probability and Statistical Inference (Hogg & Tanis)
Reference books:
Introduction to Bayesian Statistics (1st or 2nd edition), William Bolstad
Probability and Statistics for Engineering and Science, Jay Devore
Probability and Statistics for Computer Science, James L. Johnson
Syllabus (tentative):
Fundamental About Probability Theory | |
19-Feb | Introduction |
26-Feb | Axiom_prob, Conditional Prob, Independence, Bayesian |
5-Mar | Random variables, mean and variance |
12-Mar | discrete prob distribution |
19-Mar | continuous prob distribution |
26-Mar | Multivariable distribution |
2-Apr | Holiday |
9-Apr | distribution of functions |
16-Apr | Midterm |
Advanced Probability Theory | |
23-Apr | Chebyshev, Law of large numbers, central limit theorem |
30-Apr | Estimation Theory |
7-May | Information Theory |
Applications | |
14-May | Bayesian Networks |
21-May | Prob. For Data Mining and social network Analysis |
28-May | Holiday |
4-Jun | Prob. for Search and Natural langauge processing |
11-Jun | Probability and Life |
18-Jun | Final |