Probability (3 credits)


Instructor: Prof. Shou-de Lin ( , Office 333

Classroom: CSIE 104

Meeting Time: Mon 14:20-17:20 pm

Office Hour:  Mon after class or by appointment

TA: , ۾

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.

Midterm: (35%)
Final: (35%)
Competition: (30%)


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: TBA

Basic Probability Theory Competitions
17-Feb Introduction @
24-Feb Axiom_prob, Conditional Prob, Independence, Baye's Rule @
3-Mar Random variables, mean and variance @
10-Mar discrete prob distribution competition 1
17-Mar discrete prob distribution @
24-Mar Continuous Probability Distribution, Normal Distribution @
31-Mar Multivariable distributions competition 2
7-Apr Conditional distributions, correlation, independency, distribution of functions @
14-Apr Midterm@
21-Apr Chebyshev's inequality, Central Limit Theorem, Law of large number @
28-Apr Confidence Interval @
5-May Point Estimation & Chi-square fit competition 3
12-May Information Theory @
19-May Prob for Data Mining and Machine Learning @
26-May Prob for Search and Social Networks competition 4
2-Jun Break @
9-Jun Review @
16-Jun Final @