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2024　Electrical and Electronic Informatics I

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Academic unit or major
Undergraduate major in Electrical and Electronic Engineering
Instructor(s)
Arai Keigo  Amemiya Tomohiro 
Class Format
Lecture     
Media-enhanced courses
Day/Period(Room No.)
-
Group
-
Course number
EEE.M251
Credits
2
Academic year
2024
Offered quarter
3Q
Syllabus updated
2024/3/14
Lecture notes updated
-
Language used
Japanese
Access Index

Course description and aims

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.

Student learning outcomes

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 that will be developed

✔ 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/Required learning

  Course schedule Required learning
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

Out-of-Class Study Time (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

Assessment criteria and methods

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

Computational algorithms and programming are required, and Applied Probability Statistics is recommended.

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