2025 (Current Year) 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 (S2-204(S221)) / 7-8 Fri (S2-204(S221))
Class: -
Course Code: EEE.M251
Number of credits: 200
Course offered: 2025
Offered quarter: 3Q
Syllabus updated: Sep 22, 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 — Electrical/Electronic Engineering and Informatics	Theory: Understanding the course overview and objectives, and the connection between electrical/electronic engineering and information science. Practice: Setting up the Python environment (Google Colab, Jupyter Notebook) and learning basic operations (variables, print statements).
Class 2	Sets, Logic, and Data Structures	Theory: Understanding sets, propositional logic, Boolean algebra, and basic data structures (lists, stacks, queues, trees). Practice: Learning Python basic syntax (variables, operators, conditionals, loops).
Class 3	Graphs and Combinatorics	Theory: Understanding the fundamentals of graph theory (vertices, edges, trees, networks), and permutations and combinations. Practice: Learning to draw graphs and generate permutations/combinations using Python.
Class 4	Probability Theory	Theory: Understanding random variables, probability distributions, expectation and variance, and their relation to noise and communication errors. Practice: Learning to simulate dice and coin tosses using random numbers and to visualize probability distributions in Python.
Class 5	Statistical Estimation and Data Analysis	Theory: Understanding estimation, hypothesis testing, regression analysis, and their applications to measurement data processing and signal analysis. Practice: Using Pandas to aggregate data, calculate mean and correlation, and visualize results with Matplotlib.
Class 6	Computation Theory, Information Theory, and Algorithms	Theory: Understanding computability, computational complexity, automata, information content, entropy, and channel capacity. Practice: Implementing sorting and searching algorithms in Python and calculating entropy.
Class 7	Fundamentals of Information Geometry	Theory: Understanding the geometric representation of probability distributions, Kullback–Leibler divergence, and maximum likelihood estimation. Practice: Generating probability distributions and calculating KL divergence between them in Python.
Class 8	Midterm Exam + Introduction to Machine Learning	Theory: Understanding the definition of machine learning, its categories (supervised, unsupervised, semi-supervised), and the general workflow (preprocessing → learning → evaluation). Exam: Applying Python to perform basic numerical computations.
Class 9	Supervised Learning I (Regression)	Theory: Understanding the mathematics of linear regression (least squares method), overfitting and generalization, and evaluation metrics (MSE, R²). Practice: Building a linear regression model in Python and observing overfitting through polynomial regression.
Class 10	Supervised Learning II (Classification)	Theory: Understanding the framework of classification (binary and multiclass), logistic regression, k-nearest neighbors (kNN), and evaluation metrics (confusion matrix, accuracy, recall, F1 score). Practice: Implementing classifiers in Python and comparing performance with and without standardization.
Class 11	Supervised Learning III (Neural Networks + Optimization)	Theory: Understanding the perceptron, multilayer neural networks (MLPs), activation functions, and the basics of gradient descent and optimization. Practice: Training an MLP classifier in Python and examining the effects of learning rate and number of epochs.
Class 12	Unsupervised Learning I (Clustering)	Theory: Understanding clustering methods (k-means, hierarchical clustering) and their applications to sensor data classification. Practice: Executing clustering in Python and comparing results across different numbers of clusters.
Class 13	Unsupervised Learning II (Dimensionality Reduction and Feature Extraction)	Theory: Understanding PCA, t-SNE, and representation of high-dimensional data. Practice: Performing dimensionality reduction in Python, interpreting explained variance ratios, and visualizing the results.
Class 14	Semi-Supervised Learning and Applications	Theory: Understanding semi-supervised learning methods (self-training, label propagation, pseudo-labeling) and application examples (fault diagnosis, anomaly detection, sensor data analysis). Practice: Performing classification in Python with few labeled samples and many unlabeled samples.
Class 15	Summary and Outlook + Basics of Information Security	Theory: Understanding the fundamentals of information security (public-key cryptography, hashing, random number generation) and advanced applications (quantum information, AI-driven circuit design, device diagnostics).

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.