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2023　Engineering Literacy III a

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Academic unit or major
School of Engineering
Instructor(s)
Shinozaki Takahiro  Fukuda Emiko  Nakahara Hiroki  Matsumoto Ryutaroh  Takahashi Atsushi  Hara Yuko  Nagai Takehiro  Nishio Takayuki  Jitsumatsu Yutaka  Li Dongju  Jinguji Akira  Chung Su-Lin  Kawasaki Ryo  Uozumi Ryuji 
Class Format
Lecture / Exercise    (Face-to-face)
Media-enhanced courses
Day/Period(Room No.)
Thr5-6(<初回><前半>, WL2-201(W621), <後半>, 西9号館3階311号室)  
Group
a
Course number
XEG.B103
Credits
1
Academic year
2023
Offered quarter
3Q
Syllabus updated
2023/3/27
Lecture notes updated
-
Language used
Japanese
Access Index

Course description and aims

This course aims to become a bridge between general education at high school and specialized education in the subjects of No.200 or higher. This course also aims to cultivate not only basic knowledge of engineering but also sense and attitude for problem solving so that the freshmen of School of Engineering can learn actively specialized subjects after sophomore.
By taking all Engineering literacy I-IV, the students experience the following all seven subjects in Engineering Literacy.
【Water Rocket Development and Control】
【Gliding Locomotion Robot "Gyotaro-IIIa"】
【AI-Drone (Machine Learning and Motion Control)】
【Control】
【Wireless electric car with microcomputer】
【Communication, Computation, and Intelligent Information Processing】
【Industrial Engineering and Economics（Mechanism Design and Data Analysis)】

Student learning outcomes

By completing this course, students will be able to:
【Communication, Computation, and Intelligent Information Processing】
1) explain the connection between basic academics and the latest intelligent information processing techniques
2) explain the basic principle of the artificial neural network.
3) login to a supercomputer system and perform a massive computation.
【Industrial Engineering and Economics (Mechanism Design and Data Analysis)】
1) Understand the fundamental concepts of game theory and the significance of institutional design through participating economic experiment.
2) Learn how to visualize data using and interpret the background of the analysis results.

Keywords

【Communication, Computation, and Intelligent Information Processing】
Deep learning, neural network, intelligent information processing, parallel computing, communication
【Industrial Engineering and Economics (Mechanism Design and Data Analysis)】
Industrial Engineering, Game Theory, Mechanism Design, Data Analysis

Competencies that will be developed

✔ Specialist skills Intercultural skills Communication skills Critical thinking skills ✔ Practical and/or problem-solving skills

Class flow

【Communication, Computation, and Intelligent Information Processing】
This course explains the connection between the latest deep learning based intelligent information processing technique and basic academic areas such as linear algebra, calculus, probability, numeric optimization, graph theory, and biological information processing, as well as network and computer techniques that provide the computation environment. Moreover, students choose a topic from image, speech, and text processing, and run a preliminary experiment using a deep neural network on a TSUBAME super-computer or a cloud computer. The first half of this lecture explains deep learning with its connection to various academic areas and computer techniques. The latter half is an exercise where students train and evaluate a deep neural network of their choice.
【Industrial Engineering and Economics (Mechanism Design and Data Analysis)】
The lecture explains an outline of the Industrial Engineering techniques and their examples (Game Theory and Data Analysis).

Course schedule/Required learning

  Course schedule Required learning
Class 1 Introduction Conduct class interview. Learn how to use software. Learn outline of Industrial Engineering techniques (Game theory).
Class 2 Intelligent information processing I Can explain the basics of deep neural network
Class 3 Intelligent Information Processing II (Use of computer resource through Internet) Can access a crowd service through the Internet and run a script to train and evaluate a neural network.
Class 4 Intelligent Information Processing III (Train and evaluate deep neural network) Can look inside the script to get an overview and explain how neural net learning and evaluation is implemented.
Class 5 Industrial Engineering Techniques 1(Game theory) Understand the fundamental concepts of game theory and the significance of institutional design through participating economic experiment.
Class 6 Industrial Engineering Techniques 2 (Data analysis) Learn how to visualize data using an example and interpret the background of the analysis results.
Class 7 Industrial Engineering Techniques 3 (Data analysis) Learn how to visualize data using an example and interpret the background of the analysis results.

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)

None required

Reference books, course materials, etc.

Documents will be distributed

Assessment criteria and methods

【Communication, Computation, and Intelligent Information Processing】
Evaluate the report.
【Industrial Engineering and Economics (Mechanism Design and Data Analysis)】
Evaluate short reports.

Related courses

  • XEG.B101 ： Engineering Literacy I
  • XEG.B102 ： Engineering Literacy II
  • XEG.B103 ： Engineering Literacy III
  • XEG.B104 ： Engineering Literacy IV

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

This lecture is only for the freshmen of School of Engineering.
Students are strongly recommended to take all Engineering literacy I-IV to experience all subjects in Engineering Literacy.

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