2019 Probability Theory and Statistics

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Academic unit or major
Undergraduate major in Computer Science
Instructor(s)
Ishida Takashi 
Course component(s)
Lecture
Day/Period(Room No.)
Mon1-2(W611)  Thr1-2(W611)  
Group
-
Course number
CSC.T242
Credits
2
Academic year
2019
Offered quarter
1Q
Syllabus updated
2019/3/18
Lecture notes updated
-
Language used
Japanese
Access Index

Course description and aims

This course provides basic probabilistic theory and statistics. The aim of the course is learning the theorem and methods of statistics used in the field of information engineering. Students also learn how to use R-language that is a software environment for statistical computing.

Student learning outcomes

By the end of this course, students will be able to:
1) understand basic probability theory, and use probability distribution properly.
2) understand the concept of hypothesis testing, and use statistical tests properly.

Keywords

Conditional probability, expected value, variance, binomial distribution, normal distribution, Chebyshev's inequality, hypothesis testing, t-test, R-language

Competencies that will be developed

Intercultural skills Communication skills Specialist skills Critical thinking skills Practical and/or problem-solving skills

Class flow

Each class starts with the explanation of a new topic.
In the class occasionally, students are given exercise problems.
Students are asked to submit a midterm report and must take a final examination.

Course schedule/Required learning

  Course schedule Required learning
Class 1 Introduction Understanding descriptive statistics (mean, median, variance)
Class 2 Correlation Understanding correlation (correlation coefficient, liner regression)
Class 3 Introduction to R language Understanding how to process data by R-language
Class 4 Probability distribution Understanding probability and probability distribution
Class 5 Moment Understanding moment (moment, moment-generating function)
Class 6 probability inequality Understanding probability inequality (Chebyshev's inequality)
Class 7 Discrete probability distribution Understanding discrete probability distribution (binominal distribution, Bernoulli distribution, Poisson distribution)
Class 8 Continuous probability distribution Understanding continuous probability distribution (normal distribution, exponential distribution)
Class 9 Random number generation Understanding how to generate a random number (linear congruent method, Box-Muller’s method
Class 10 law of great numbers Understanding a law of great numbers (Independent and identically distributed, a law of great numbers, central limit theorem)
Class 11 Statistical inference Understanding statistical inference (point estimation, moment method, maximum-likelihood method)
Class 12 Hypothesis testing Understanding hypothesis testing (significance level, type I/II error)
Class 13 t-test Understanding t-test (Student’s t-test, Welch’s t-test)
Class 14 Chi-squared test Understanding chi-squared test (F-test, chi-squared test)
Class 15 Statistic-test by R Understanding how to perform statistic-test by R

Textbook(s)

N/A

Reference books, course materials, etc.

N/A

Assessment criteria and methods

Students' knowledge and their ability will be assessed mainly by a midterm report and final examination. The weight of the midterm report is equal to that of the final examination.

Related courses

  • CSC.T272 : Artificial Intelligence
  • CSC.T352 : Pattern Recognition
  • CSC.T353 : Biological Data Analysis

Prerequisites (i.e., required knowledge, skills, courses, etc.)

None

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