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School of Engineering
- Undergraduate major in Mechanical Engineering
- Undergraduate major in Systems and Control Engineering
- Undergraduate major in Electrical and Electronic Engineering
- Undergraduate major in Information and Communications Engineering
- Undergraduate major in Industrial Engineering and Economics
- Common courses
- School of Materials and Chemical Technology
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- 工学院，物質理工学院，環境・社会理工学院共通科目
- Liberal arts and basic science courses
- First-Year Courses
- School of Science
- School of Engineering
- School of Materials and Chemical Technology
- School of Computing
- School of Life Science and Technology
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School of Environment and Society
- Department of Architecture and Building Engineering
- Department of Civil and Environmental Engineering
- Department of Transdisciplinary Science and Engineering
- Department of Social and Human Sciences
- Department of Innovation Science
- Graduate major in Technology and Innovation Management
- Common courses
- Liberal arts and basic science courses
- Academic unit or major
- Undergraduate major in Electrical and Electronic Engineering
- Instructor(s)
- Kajikawa Kotaro Kawano Yukio
- Class Format
- Lecture
- Media-enhanced courses
- Day/Period(Room No.)
- Mon7-8(S221) Fri3-4(S221)
- Group
- -
- Course number
- EEE.M231
- Credits
- 2
- Academic year
- 2016
- Offered quarter
- 3Q
- Syllabus updated
- 2016/4/27
- Lecture notes updated
- -
- Language used
- Japanese
- Access Index
-
Course description and aims
It is vital to acquire knowledge and skills in statistics and probability in various fields related to electrical engineering and information communication engineering. By combining lectures and exercises, the course enables students to understand and learn the fundamentals of mean, variance, characteristic function, etc. in the first part (probability) and those of unbiased estimation, maximum likelihood estimation, hypothesis testing, etc. in the second part (statistics).
The ability to derive statistically significant information will be very useful in the real world.
Student learning outcomes
Students will be able to learn how to analyze data in various fields related to electrical engineering and information communication engineering by using probablistic methods and statistical techniques. The course provides specific examples in engineering, which will give a deeper understanding. Many practical exercises and exams will enable students to acquire knowledge effectively.
Keywords
Probability, Mean, Variance, Maximum likelihood estimation, Hypothesis testing
Competencies that will be developed
| ✔ Specialist skills | Intercultural skills | Communication skills | ✔ Critical thinking skills | ✔ Practical and/or problem-solving skills |
Class flow
To cultivate practical ability, students are given many exercise problems, which are related to a previous class and a class on the day.
Course schedule/Required learning
| Course schedule | Required learning | |
|---|---|---|
| Class 1 | Permutations | Understand the Permutations |
| Class 2 | Conditional probability | Calculate conditional probability. |
| Class 3 | Mean and variance | Calculate mean and variance |
| Class 4 | Characteristic function | Understand characteristic function |
| Class 5 | Random variable and distribution I | Understand random variable and distribution |
| Class 6 | Random variable and distribution II | Understand random variable and distribution |
| Class 7 | Random variable and distribution III | Understand random variable and distribution |
| Class 8 | Test level of understanding with exercise problems | Test level of understanding for classes 1–7 |
| Class 9 | Review of the first part of the course | Review the first part (probability) and understand the introduction to the second part (statistics) |
| Class 10 | Statistical estimation | Calculate several types of means |
| Class 11 | Unbiased estimation | Understand unbiased estimation |
| Class 12 | Maximum likelihood estimation | Learn how to perform maximum likelihood estimation |
| Class 13 | Hypothesis testing | Learn applications of hypothesis testing |
| Class 14 | Stochastic process I | Perform regression analysis calculation |
| Class 15 | Stochastic process II | Calculate correlation coefficient |
Textbook(s)
N/A
Reference books, course materials, etc.
Watanabe, Sumio, Noboru, Murata, Probability and statistics: a bridge to information science, Corona-sha; ISBN: 9784339060775 (in Japanese)
Ogura, Hisanao, stochastic process for physics and engineering, Corona-sha; ISBN: 9784339004229, 9784339004236 (in Japanese)
Shibata, Fumiaki, Probability and statistics, Iwanami-shoten; ISBN: 9784000079778 (in Japanese)
Baba, Noriyuki, Kuchii, Shigeru, Campus seminar: statistics, mathema-shuppan, ISBN: 9784907165314 (in Japanese)
Assessment criteria and methods
Students' knowledge of probability and statistical, and their ability to apply them to problems will be assessed.
Midterm exam 40%, final exam 40%, exercise problems 20%.
Related courses
- LAS.M101 ： Calculus I / Recitation
- LAS.M105 ： Calculus II
- EEE.M211 ： Fourier Transform and Laplace Transform
- EEE.S341 ： Communication Theory (Electrical and Electronic Engineering)
Prerequisites (i.e., required knowledge, skills, courses, etc.)
Students must have successfully completed both Calculus I and Calculus II.
Other
Contact by e-mail in advance.
