2022 Faculty Courses School of Engineering Undergraduate major in Systems and Control Engineering
Fundamentals of Machine Learning
- Academic unit or major
- Undergraduate major in Systems and Control Engineering
- Instructor(s)
- Katsutoshi Itoyama
- Class Format
- Lecture (Face-to-face)
- Media-enhanced courses
- -
- Day of week/Period
(Classrooms) - 5-6 Tue (W933)
- Class
- -
- Course Code
- SCE.I352
- Number of credits
- 100
- Course offered
- 2022
- Offered quarter
- 3Q
- Syllabus updated
- Jul 10, 2025
- Language
- Japanese
Syllabus
Course overview and goals
In this lecture, we will learn some basic methods in machine learning, their mathematical derivations, and their implementation on computers.
Course description and aims
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
- 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/Objectives
| Course schedule | Objectives | |
|---|---|---|
| Class 1 | Introduction | Basics of mathematics required for machine learning |
| Class 2 | Linear models (Regression) | Linear regression model and its learning method |
| Class 3 | Linear Models (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) |
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 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
Evaluation methods and criteria
Students will be assessed on their understanding of the basic theory of machine learning and its application. Exercise problems 70%, final examination 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
It is desirable to have a basic knowledge of linear algebra and calculus.