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 and clustering.
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.
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.
Point estimation, interval estimation, hypothesis testing, multivariate analysis, classification, clustering
✔ Specialist skills | Intercultural skills | Communication skills | ✔ Critical thinking skills | ✔ Practical and/or problem-solving skills |
Give a lecture and give some exercise problems. Solutions for the exercise problems are also reviewed.
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 (1) | Understand basics of hypothesis testing |
Class 6 | Hypothesis testing (2) | Understand hypothesis testing methods for various settings |
Class 7 | Bivariate data | Understand statistical methods for handling bivariate data |
Class 8 | Multivariate analysis | Understand multivariate analysis methods |
Class 9 | Basics of machine learning | Understand basics of machine learning |
Class 10 | Classification | Understand data classification methods |
Class 11 | Predictive performance evaluation | Understand how to evaluate the performance of machine learning methods |
Class 12 | Clustering | Understand data clustering methods |
Class 13 | Computational leaning theory | Understand basic learning theory of machine learning |
Class 14 | Conclusion | Understand how to apply statistical methods to engineering problems |
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.
Nobuaki Obata: Probability and Statistics for Data Science, Kyoritsu Shuppan (in Japanese)
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.
Exercise problems and Final exam.
Students must have successfully completed "Probability for Industrial Engineering and Economics" or have equivalent knowledge.