2022 Faculty Courses School of Engineering Undergraduate major in Industrial Engineering and Economics
Statistics for Industrial Engineering and Economics
- Academic unit or major
- Undergraduate major in Industrial Engineering and Economics
- Instructor(s)
- Ryutaro Ichise
- Class Format
- Lecture/Exercise (Livestream)
- Media-enhanced courses
- -
- Day of week/Period
(Classrooms) - 1-2 Tue (W935) / 1-2 Fri (W935)
- Class
- -
- Course Code
- IEE.A205
- Number of credits
- 110
- Course offered
- 2022
- Offered quarter
- 2Q
- Syllabus updated
- Jul 10, 2025
- Language
- Japanese
Syllabus
Course overview and goals
In this lecture, we will discuss statistical methods and machine learning methods, which form the core of data science, as engineering approaches to solving various industrial engineering and economics problems. In the field of statistics, we will discuss mean and variance, statistical estimation, statistical hypothesis testing, and multivariate analysis. In the field of machine learning, we will discuss classification, clustering, and dimension reduction.
In this lecture, students will acquire basic knowledge of statistical views and ideas that form the basis of data science, frameworks for statistical decision-making, and basic machine learning methods. In addition, students will be able to apply them to industrial engineering and economics problem-solving.
Course description and aims
By taking this course, students will be able to acquire the following skills.
(1) Basic knowledge of statistical estimation, statistical testing, and machine learning for data handling methods.
(2) To be able to calculate, interpret, and explain basic statistics using numbers and figures.
(3) To be able to use data to solve engineering problems using statistical and machine learning methods.
Keywords
Point estimation, interval estimation, hypothesis testing, multivariate analysis, classification, clustering, dimension reduction
Competencies
- Specialist skills
- Intercultural skills
- Communication skills
- Critical thinking skills
- Practical and/or problem-solving skills
Class flow
Give a lecture and give some exercise problems. Solutions for the exercise problems are also reviewed.
Course schedule/Objectives
| Course schedule | Objectives | |
|---|---|---|
| Class 1 | Basics of Statistics |
Understand basics of statistics |
| Class 2 | Probability |
Understand random variables and probability distributions |
| Class 3 | Sampling & Estimation |
Understand sampling, point estimation and law of large numbers |
| Class 4 | Interval estimation |
Understand interval estimation methods for various settings |
| Class 5 | Hypothesis testing |
Understand hypothesis testing methods for various settings |
| Class 6 | Bivariate data |
Understand statistical methods for handling bivariate data |
| Class 7 | Multivariate analysis |
Understand multivariate analysis methods |
| Class 8 | Basics of machine learning |
Understand basics of machine learning |
| Class 9 | Classification |
Understand data classification methods |
| Class 10 | Predictive performance evaluation |
Understand how to evaluate the performance of machine learning methods |
| Class 11 | Clustering |
Understand data clustering methods |
| Class 12 | Dimension reduction |
Understand methods of dimension reduction for large scale data |
| Class 13 | Computational leaning theory |
Understand basic learning theory of machine learning |
| Class 14 | Conclusion |
Understand how to apply statistical methods to engineering problems |
Study advice (preparation and review)
To enhance effective learning, students are encouraged to spend approximately 100 minutes preparing for class and another 100 minutes reviewing class content afterward (including assignments) for each class.
Textbook(s)
Nobuaki Obata: Probability and Statistics for Data Science, Kyoritsu Shuppan (in Japanese)
Reference books, course materials, etc.
Akira Suzuki: Algorithm for Machine Leaning, Kyoritsu Shuppan (in Japanese)
Kazunori Matsumoto, Tetsuhiro Miyahara, Yasuo Nagai, Ryutaro Ichise: Artificial Intelligence, Ohm Sha (in Japanese)
Provide handouts when needed.
Evaluation methods and criteria
Exercise problems and Final exam.
Related courses
- IEE.A204 ： Probability for Industrial Engineering and Economics
- IEE.A331 ： OR and Modeling
- IEE.C302 ： Quality Management
Prerequisites
Students must have successfully completed "Probability for Industrial Engineering and Economics" or have equivalent knowledge.