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2024　Electrical and Electronic Informatics II

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Academic unit or major
Undergraduate major in Electrical and Electronic Engineering
Instructor(s)
Amemiya Tomohiro  Arai Keigo 
Class Format
Lecture     
Media-enhanced courses
Day/Period(Room No.)
-
Group
-
Course number
EEE.M252
Credits
2
Academic year
2024
Offered quarter
4Q
Syllabus updated
2024/3/14
Lecture notes updated
-
Language used
Japanese
Access Index

Course description and aims

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.

Student learning outcomes

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 that will be developed

✔ 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/Required learning

  Course schedule Required learning
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

Out-of-Class Study Time (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)
 拡散モデル データ生成技術の数理（岩波書店）(in Japanese)

Assessment criteria and methods

Grades will be assigned as follows.
 Attendance report (40%)
 Midterm: Submission of Python code (30%)
 Final exam (30%)

Related courses

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

Prerequisites (i.e., required knowledge, skills, courses, etc.)

Just a little motivation and passion

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

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