2024 Faculty Courses School of Engineering Undergraduate major in Electrical and Electronic Engineering
Electrical and Electronic Informatics I
- Academic unit or major
- Undergraduate major in Electrical and Electronic Engineering
- Instructor(s)
- Keigo Arai / Tomohiro Amemiya
- Class Format
- Lecture (Face-to-face)
- Media-enhanced courses
- -
- Day of week/Period
(Classrooms) - 7-8 Tue / 7-8 Fri
- Class
- -
- Course Code
- EEE.M251
- Number of credits
- 200
- Course offered
- 2024
- Offered quarter
- 3Q
- Syllabus updated
- Mar 17, 2025
- Language
- Japanese
Syllabus
Course overview and goals
In this course, you will learn the basic concepts of informatics and methods of numerical calculation. The basic concept of informatics is important knowledge for conducting research in various fields of electrical and electronic systems. Through lectures and exercises, the purpose is to broadly understand and master the analysis and utilization of data and information obtained in electrical and electronic research, information mathematics, computational geometry, measurement and analysis, and machine learning. In addition, by learning Python, which is a general-purpose language in various fields of informatics, we aim to use it for numerical analysis in other situations such as other courses, experiments, and advanced research. Therefore, students develop perspectives for incorporating information science into electrical and electronic research.
Course description and aims
By taking this course, students will acquire the following abilities.
1) Have knowledge of the theory of information mathematics, computational geometry, metric and analysis, which are the basic concepts of information science.
2) Be able to handle various methods of machine learning.
3) Be able to perform simple numerical calculations on the above items using Python.
The corresponding learning goals are
(1) [Expertise] Fundamental expertise
(4) [Development ability] (inquiry or setting ability) Ability to organize and analyze
(7) Ability to acquire a wide range of specialized knowledge and expand learning independently into more advanced specialized fields and other fields
Keywords
Information Mathematics, Computational Geometry, Measurement, Machine Learning, Python
Competencies
- Specialist skills
- Intercultural skills
- Communication skills
- Critical thinking skills
- Practical and/or problem-solving skills
- Applied expertise in electrical and electronic fields
Class flow
At the beginning of each lecture, a simple exercise and commentary on the contents of the previous lecture will be given in order to improve understanding. In addition, in order to acquire practical numerical calculation skills, we will interweave exercises using Python.
Course schedule/Objectives
| Course schedule | Objectives | |
|---|---|---|
| Class 1 | Introduction to Information Science 1: Introduction | An overview of information science and an understanding of the basic concepts of information mathematics (sets/logic, integers, graphs, combinatorics) |
| Class 2 | Exercises | Preparing the Python environment, basic syntax, and mastering scientific computing libraries |
| Class 3 | Introduction to Information Science 2: Theory of Computation | Understanding computational complexity, decision problems, and algorithmic evaluation |
| Class 4 | Introduction to Information Science 3: Information Geometry and Metric/Analysis | A review of probability statistics and an understanding of the basic concepts of stochastic processes and information metrics |
| Class 5 | Exercises | A computational implementation of informatics concepts in Python. Acquisition of visualization techniques |
| Class 6 | Machine Learning 1: Supervised Learning (Regression) | Understanding linear regression theory, overfitting, and regularization methods |
| Class 7 | Machine Learning 2: Supervised Learning (Classification) | Understand logistic regression and SVM techniques |
| Class 8 | Exercises | Exercises in supervised learning |
| Class 9 | Mid-term exam | Numerical calculation using Python |
| Class 10 | Machine Learning 3: Unsupervised Learning (Dimensionality Reduction) | Understanding of dimensionality reduction concepts and implementation methods (PCA, NMF, etc.) |
| Class 11 | Machine Learning 4: Unsupervised Learning (Clustering) | Understanding of clustering concepts and implementation methods (k-means method, Gaussian mixture model, etc.) |
| Class 12 | Exercises | Exercises in unsupervised learning |
| Class 13 | Machine Learning 5: Semi-Supervised Learning | Understanding combined methods of supervised/unsupervised learning (consistency regularization and entropy minimization) |
| Class 14 | Machine Learning 6: Introduction to Reinforcement Learning | Understand the basic concepts of reinforcement learning |
| Class 15 | General exercises on Information Science and Machine Learning | Exercises covering the whole contents and confirmation of the understanding on basic concepts about information science and machine learning |
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)
Not applicable
Reference books, course materials, etc.
Reference book: “Learn by moving with Python! A new machine learning textbook” by Makoto Ito, Shoeisha
Evaluation methods and criteria
Comprehension of the basic theory of information science and related fields, and proficiency in numerical calculation using Python will be evaluated. In addition to exercises (40%) every time, to check understanding and proficiency, grades will be evaluated by a midterm exam (30%) of numerical calculation using Python and a final report (30%). .
Related courses
- EEE.M221 ： Computation Algorithms and Programming
- EEE.M231 ： Applied Probability and Statistical Theory
- EEE.S341 ： Communication Theory (Electrical and Electronic Engineering)
- EEE.S351 ： Signal System
- EEE.M252 ： Electrical and Electronic Informatics II
Prerequisites
Computational algorithms and programming are required, and Applied Probability Statistics is recommended.