2025 (Current Year) Faculty Courses School of Engineering Undergraduate major in Electrical and Electronic Engineering

Electrical and Electronic Informatics II

Academic unit or major: Undergraduate major in Electrical and Electronic Engineering
Instructor(s): Tomohiro Amemiya / Keigo Arai
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.M252
Number of credits: 200
Course offered: 2025
Offered quarter: 4Q
Syllabus updated: Oct 7, 2025
Language: Japanese

Syllabus

Course overview and goals

In addition to the machine learning methods learned in "EEE.M251: Electrical and Electronic Informatics I", this course (EEE.M252) covers various algorithms for deep learning. An understanding of the basic concepts of deep learning is important for future research and development in various fields of electrical and electronic systems.

Course description and aims

Students will acquire the following abilities.
1) Deepen understanding of various algorithms for deep learning.
2) To be able to code a simple neural network in Python.

Keywords

AI, deep learning, Python

Competencies

Specialist skills
Intercultural skills
Communication skills
Critical thinking skills
Practical and/or problem-solving skills
Applied professional capacity in electrical and electronic fields

Class flow

In each lecture, explanations will be given using specified slides.
In addition, there will be 2-3 guest lectures.

Course schedule/Objectives

	Course schedule	Objectives
Class 1	Introduction	Introduction
Class 2	Concepts of Deep Learning	Deepen understanding of the overall overview of deep learn
Class 3	Loss function	Learn about typical loss function used for deep networks
Class 4	Back Propagation method	Learn about Back Propagation method.
Class 5	Convolutional neural networks (CNN)	Learn the basics of convolutional neural networks for image data processing
Class 6	Vanishing gradient problem and Convergence in deep learning I	Learn about vanishing gradient problem and convergence.
Class 7	Vanishing gradient problem and Convergence in deep learning II	Learn about vanishing gradient problem and convergence.
Class 8	Guest Lecture	Guest Lecture
Class 9	Recurrent neural network	Learn about recursive neural networks for time series data processing
Class 10	Attention mechanism and Transformer I	Explain the details of the Attention Mechanism and Transformer.
Class 11	Attention mechanism and Transformer II	Explain the details of the Attention Mechanism and Transformer.
Class 12	Guest Lecture	Guest Lecture
Class 13	Generative model I	Learn about variational auto-encoder (VAE).
Class 14	Generative model II	Learn about typical diffusion model.
Class 15	Guest Lecture	Guest Lecture

Study advice (preparation and review)

Basically, no preparation is required, but it is strongly recommended to read through the relevant sections of reference books to deepen your understanding.

Textbook(s)

In each lecture, explanations will be given using specified slides.

Reference books, course materials, etc.

 Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems (O'Reilly)
 深層学習改訂第2版 (機械学習プロフェッショナルシリーズ)（講談社）(in Japanese)
 ゼロから作るDeep Learning 5 ― 生成モデル（オライリー）(in Japanese)

Evaluation methods and criteria

Grades will be assigned as follows.
 Midterm: Submission of Python code (50%)
 Final: Submission of Python code (50%)

Related courses

EEE.M221 ： Computation Algorithms and Programming
EEE.M231 ： Applied Probability and Statistical Theory
EEE.M251 ： Electrical and Electronic Informatics I

Prerequisites

Just a little motivation and passion

EEE.M251 Electrical and Electronic Informatics I : Recommended
EEE.M221 Computation Algorithms and Programming : Recommended
EEE.M231 Applied Probability and Statistical Theory : Recommended