2022 Faculty Courses School of Engineering First-Year Courses
Engineering Literacy III a
- Academic unit or major
- First-Year Courses
- Instructor(s)
- Takahiro Shinozaki / Hiroki Nakahara / Ryutaroh Matsumoto / Atsushi Takahashi / Yuko Hara / Takehiro Nagai / Jaehoon Yu / Natsue Yoshimura / Dongju Li / Xiuzhu Gu / Emiko Fukuda / Ryo Kawasaki / Akira Jinguji
- Class Format
- Lecture/Exercise (Face-to-face)
- Media-enhanced courses
- -
- Day of week/Period
(Classrooms) - 5-6 Thu (<前半>, W621, <後半>, 西9号館3階311号室)
- Class
- a
- Course Code
- XEG.B103
- Number of credits
- 0.50.50
- Course offered
- 2022
- Offered quarter
- 3Q
- Syllabus updated
- Jul 10, 2025
- Language
- Japanese
Syllabus
Course overview and goals
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.
【Manufacturing process 】
【Mechanical Design (Idea Realization)】
【Mechanical Design (CAD)】
【Control】
【Wireless electric car with microcomputer】
【Communication, Computation, and Intelligent Information Processing】
【Management Technology and Mechanism Design (Industrial Engineering and Economics)】
Course description and aims
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.
【Management Technology and Mechanism Design (Industrial Engineering and Economics)】
1) understand the outline of Industrial Engineering (IE) techniques.
2) apply IE techniques to simple cases.
3) understand the fundamental concepts of game theory and the significance of institutional design through participating economic experiment.
Keywords
【Communication, Computation, and Intelligent Information Processing】
Deep learning, neural network, intelligent information processing, parallel computing, communication
【Management Technology and Mechanism Design (Industrial Engineering and Economics)】
Management Technology, Industrial Engineering, Cognitive Engineering, Game Theory, Mechanism Design
Competencies
- 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.
【Management Technology and Mechanism Design (Industrial Engineering and Economics)】
The lecture explains an outline of the Industrial Engineering techniques and their examples (Cognitive Engineering and Game Theory).
Course schedule/Objectives
| Course schedule | Objectives | |
|---|---|---|
| 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(Cognitive task analysis) | Carry out an evaluation of interface by applying cognitive task analysis technique. |
| Class 7 | Industrial Engineering Techniques 3(Cognitive task analysis) | Carry out an evaluation of interface by applying cognitive task analysis technique. |
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)
None required
Reference books, course materials, etc.
Documents will be distributed
Evaluation methods and criteria
【Communication, Computation, and Intelligent Information Processing】
Evaluate the report.
【Management Technology and Mechanism Design (Industrial Engineering and Economics)】
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
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.