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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
-
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 Mechanical Engineering
- Instructor(s)
- Miura Satoshi
- Class Format
- Lecture / Exercise (Face-to-face)
- Media-enhanced courses
- Day/Period(Room No.)
- -
- Group
- -
- Course number
- MEC.B221
- Credits
- 2
- Academic year
- 2024
- Offered quarter
- 3Q
- Syllabus updated
- 2024/3/14
- Lecture notes updated
- -
- Language used
- Japanese
- Access Index
-
Course description and aims
The Genearal Instrctuve Objective is to study the data processing method using statistics, optimzation and machine learning by the theoreotical and pratical method.
Student learning outcomes
The specific behavioral objectives of this course are as follows
To be able to understand, explain, and practice basic statistical processing for data variability.
To be able to understand, explain, and practice basic optimization calculations.
Understand, explain, and practice basic machine learning.
Keywords
Machine learning, AI, Statistics, Probability, Disperson, Data science, Deep learning, Artificial Intelligence, Optimization
Competencies that will be developed
| ✔ Specialist skills | Intercultural skills | Communication skills | ✔ Critical thinking skills | Practical and/or problem-solving skills |
| ✔ This course corresponds to the following learning objectives: 6. developmental expertise in mechanical engineering 7. the ability to utilize specialized knowledge to solve new problems and make creative proposals. | ||||
Class flow
After the lectures, students will connect to the Internet on their own PCs and practice programming. Please prepare for and review each lecture.
Course schedule/Required learning
| Course schedule | Required learning | |
|---|---|---|
| Class 1 | Introduction | Population and sample space, Bayes' theorem, etc. |
| Class 2 | Data Variability | Probability dispersion function, correlation coefficient |
| Class 3 | Interpretation of Variation | Probability distribution, mean and variance, central limit theorem |
| Class 4 | Representative data and various probability distributions | Binomial, Poisson, and normal distributions |
| Class 5 | statistical testing methods | Comparison of population means, significant differences, p-values, confidence intervals |
| Class 6 | Data-based estimation methods | Maximum Likelihood Estimation Method, Information Criterion and Model Selection Theory, etc. |
| Class 7 | data modeling | Generalized linear models, linear regression, logistic regression, etc. |
| Class 8 | Optimized data visualization methods | Dimensional Compression, Principal Component Analysis, MDS, other manifold learning |
| Class 9 | clustering approach | Hierarchical, group mean, etc., non-hierarchical, k-means, etc. |
| Class 10 | Robust Estimation Methods | Least squares, M-estimation, robust estimation using random numbers, etc. |
| Class 11 | Developmental modeling using machine learning (1) | From recent and advanced topics such as nonlinear models, Bayesian networks |
| Class 12 | Developmental modeling using machine learning (2) | From recent and advanced topics such as nonlinear models and neural networks |
| Class 13 | Data science-aware research design, etc. | DNN, CNN, RNN, GAN, Transfer learning |
| Class 14 | Final Report | Final Report |
| Class 15 | - |
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 afterwards (including assignments) for each class.
They should do so by referring to textbooks and other course material.
Textbook(s)
Distribute materials as needed.
Reference books, course materials, etc.
none
Assessment criteria and methods
Evaluation will be based on submissions in the form of reports.
Related courses
- MEC.B231 ： Probability Theory and Statistics
- MEC.B232 ： Fundamentals of Numerical Analysis
- MEC.B201 ： Fundamentals of information and mathematical sciences
Prerequisites (i.e., required knowledge, skills, courses, etc.)
This course is part of
B231.L "Probability and Statistics" and MEC.B232.L "Basic Numerical Methods
L "Probability and Statistics" and "Fundamental Numerical Methods".
L "Probability and Statistics" and the former MEC.
Students who entered before March 31, 2023 (～22B) cannot take this course if they have already earned credits in both "Probability and Statistics" and "Fundamental Numerical Methods".
1 credit of L (elective) and 1 credit of non-standard course if the student has already earned credits for either of the two subjects.
If the student has not earned both credits, 2 L (elective) credits
If both credits have not been earned, the credits will be converted at the rate of 2 L (elective) credits.
Other
MEC.B201 : Students should have taken Fundamentals of Informatics and Mathematics or have equivalent knowledge.
