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2022　Statistics for Industrial Engineering and Economics

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Academic unit or major
Undergraduate major in Industrial Engineering and Economics
Instructor(s)
Ichise Ryutaro 
Class Format
Lecture / Exercise    (Livestream)
Media-enhanced courses
Day/Period(Room No.)
Tue1-2(W935)  Fri1-2(W935)  
Group
-
Course number
IEE.A205
Credits
2
Academic year
2022
Offered quarter
2Q
Syllabus updated
2022/3/16
Lecture notes updated
-
Language used
Japanese
Access Index

Course description and aims

 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.

Student learning outcomes

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 that will be developed

✔ 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/Required learning

  Course schedule Required learning
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

Out-of-Class Study Time (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.

Assessment criteria and methods

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

 Students must have successfully completed "Probability for Industrial Engineering and Economics" or have equivalent knowledge.

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