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: 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: (40%
)

Final: (40%)

Project: (20%)

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21ら | Introduction |

2る28ら | no class |

3る7ら | Axiom_prob, Conditional Prob, Independence, Baye's Rule |

3る14ら | Random variables, mean and variance |

3る21ら | discrete prob distribution |

3る28ら | Continuous Probability Distribution, Normal Distribution |

4る4ら | break |

4る11ら | Multivariable distributions |

4る18ら | Midterm |

4る25ら | Conditional distributions, correlation, independency, distribution of functions |

5る2ら | Chebyshev's inequality, Central Limit Theorem, Law of large number |

5る9ら | Final Project Proposal |

5る16ら | Central Limit Theorem, estimation, chi-square |

5る23ら | Confidence Interval + Hypothesis Test |

5る30ら | Information Theory |

6る6ら | Language models & others, Probability & Life |

6る13ら | Final Project Presentation/Demo |

6る20ら | Final Exam |