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, Bayesian inference, hypothesis testing, machine learning, etc. in the second part (statistics).
The ability to derive statistically significant information will be very useful in the real world.
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.
Corresponding educational goals are:
(1) Specialist skills Fundamental specialist skills
(6) Firm fundamental specialist skills on electrical and electronic engineering, including areas such as electromagnetism, circuits, linear systems, and applied mathematics
Probability, Mean, Variance, Maximum likelihood estimation, Bayesian inference, Hypothesis testing, Machine learning
|✔ Specialist skills||Intercultural skills||Communication skills||Critical thinking skills||Practical and/or problem-solving skills|
|✔ ・Fundamental specialist skills on EEE|
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|
|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||Test level of understanding with exercise problems||Test level of understanding for classes 1–6|
|Class 8||Statistical inference||Understand data analysis by statistical inference|
|Class 9||Unbiased estimation||Understand unbiased estimation|
|Class 10||Maximum likelihood estimation and Bayesian inference||Learn how to perform maximum likelihood estimation and Bayesian inference|
|Class 11||Hypothesis testing, Classification, Machine learning||Learn how to perform hypothesis test and classification|
|Class 12||Regression, Prediction, Machine learning||Learn how to perform regression and prediction|
|Class 13||Stochastic process||Understand stochastic process and method of data analysis|
|Class 14||Test level of understanding with exercise problems||Test level of understanding for classes 8–13|
To enhance effective learning, students are encouraged to spend approximately 100 minutes preparing for class and another 100 minutes reviewing class content afterwards (including assignments) for each class.
They should do so by referring to textbooks and other course material.
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)
Suyama, Atsushi, Machine learning based on Bayesian inference, Kodansha; ISBN: 9784061538320 (in Japanese)
Students' knowledge of probability and statistical, and their ability to apply them to problems will be assessed.
Exam 50%, exercise problems 50%.
Students must have successfully completed both Calculus I and Calculus II.
Contact by e-mail in advance.