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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
- School of Computing
- School of Life Science and Technology
- School of Environment and Society
- 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 Systems and Control Engineering
- Instructor(s)
- Itoyama Katsutoshi
- Class Format
- Lecture
- Media-enhanced courses
- Day/Period(Room No.)
- Fri5-6(W641)
- Group
- -
- Course number
- SCE.I352
- Credits
- 1
- Academic year
- 2021
- Offered quarter
- 3Q
- Syllabus updated
- 2021/3/19
- Lecture notes updated
- -
- Language used
- Japanese
- Access Index
-
Course description and aims
In this lecture, we will learn some basic methods in machine learning, their mathematical derivations, and their implementation on computers.
Student learning outcomes
Learn about neural networks and clustering, which are the basic methods of machine learning, and acquire the mathematical basics for deriving them so that they can be implemented and operated on a computer.
Keywords
Machine Learning, Artificial Intelligence, Deep Learning, Pattern Recognition
Competencies that will be developed
| ✔ Specialist skills | Intercultural skills | Communication skills | Critical thinking skills | ✔ Practical and/or problem-solving skills |
Class flow
Classes mainly consist of lectures and exercises.
Course schedule/Required learning
| Course schedule | Required learning | |
|---|---|---|
| Class 1 | Introduction | Basics of mathematics required for machine learning |
| Class 2 | Linear Regression | Linear regression model and its learning method |
| Class 3 | Classification | Logistic regression, support vector machine |
| Class 4 | Neural Network (Basic) | Perceptron, activation function, back propagation method |
| Class 5 | Neural Network (Advanced) | Convolutional neural network (CNN), recurrent neural network (RNN) |
| Class 6 | Clustering | k-means, Gaussian mixture model (GMM) |
| Class 7 | Matrix Decomposition | Principal component analysis (PCA), independent component analysis (ICA), non-negative matrix factorization (NMF) |
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)
No textbook used
Reference books, course materials, etc.
Reference Book: Pattern Recognition and Machine Learning, C. M. Bishop, Springer
Assessment criteria and methods
Students will be assessed on their understanding of the basic theory of machine learning and its application. Exercise problems 70%, final report 30%.
Related courses
- SCE.I204 ： Information Processing and Programming (Systems and Control)
- SCE.I205 ： Fundamentals of Data Science
- LAS.M101 ： Calculus I / Recitation
- LAS.M102 ： Linear Algebra I / Recitation
- SCE.M307 ： Image Sensing
Prerequisites (i.e., required knowledge, skills, courses, etc.)
It is desirable to have a basic knowledge of linear algebra and calculus.
