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: Cayon Liow Keei Yann cayon.1318.96@hotmail.com, Li-Wei Chang r08922041@g.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: (35%
)
Final: (35%)
Project: (30%)
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る20ら | Introduction |
2る27ら | Axiom_prob, Conditional Prob, Independence, Baye's Rule |
3る5ら | Random variables, mean and variance |
3る12ら | discrete prob distribution |
3る19ら | Continuous Probability Distribution, Normal Distribution |
3る26ら | Multivariable distributions |
4る2ら | break |
4る9ら | Midterm |
4る16ら | Conditional distributions, correlation, independency, distribution of functions |
4る23ら | Chebyshev's inequality, Central Limit Theorem, Law of large number |
4る30ら | Final Project Proposal |
5る7ら | Central Limit Theorem, estimation, chi-square |
5る14ら | Confidence Interval + Hypothesis Test |
5る21ら | Information Theory |
5る28ら | Final Project Presentation 1 |
6る4ら | Language models & others, Probability & Life |
6る11ら | Final Exam |
6る18ら | Final Project Presentation 2 |