2025 (Current Year) Faculty Courses School of Engineering First-Year Courses
Engineering Literacy I f
- Academic unit or major
- First-Year Courses
- Instructor(s)
- Ryuji Uozumi / Kazuhiko Fukawa / Hiroshi Morita / Ken Kobayashi / Takeo Hori / Kenta Kasai / Takahiro Shinozaki / Atsushi Takahashi / Takehiro Nagai / Takayuki Nishio / Yuko Hara / Daichi Fujiki / Ryutaroh Matsumoto / Dongju Li
- Class Format
- Lecture/Exercise (Face-to-face)
- Media-enhanced courses
- -
- Day of week/Period
(Classrooms) - 5-6 Tue
- Class
- f
- Course Code
- XEG.B101
- Number of credits
- 0.50.50
- Course offered
- 2025
- Offered quarter
- 1Q
- Syllabus updated
- Apr 3, 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.
【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 (Macroeconomics and Data Analysis)】
Course description and aims
By completing this course, students will be able to:
【Industrial Engineering and Economics (Macroeconomics and Data Analysis)】
1) Understand the fundamental concepts of macroeconomics through computer simulation.
2) Learn how to visualize data using and interpret the background of the analysis results.
【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.
Keywords
【Industrial Engineering and Economics (Macroeconomics and Data Analysis)】
Industrial Engineering, Macroeconomics, Data Analysis
【Communication, Computation, and Intelligent Information Processing】
Deep learning, neural network, intelligent information processing, parallel computing, communication
Competencies
- Specialist skills
- Intercultural skills
- Communication skills
- Critical thinking skills
- Practical and/or problem-solving skills
Class flow
【Industrial Engineering and Economics (Macroeconomics and Data Analysis)】
The lecture explains an outline of the Industrial Engineering techniques and their examples (Macroeconomics and Data Analysis). Students are required to bring their laptop.
【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.
Course schedule/Objectives
| Course schedule | Objectives | |
|---|---|---|
| Class 1 | Introduction | Conduct class interview. Learn outline of Industrial Engineering techniques (Macroeconomics). Learn how to use software. |
| Class 2 | Industrial Engineering Techniques 1(Macroeconomics) | Understand the fundamental concepts of macroeconomics through computer simulation. |
| Class 3 | Industrial Engineering Techniques 2 (Data analysis) | Learn how to visualize data using an example and interpret the background of the analysis results. Students are required to bring their laptop with Microsoft Excel. |
| Class 4 | Industrial Engineering Techniques 3 (Data analysis) | Learn how to visualize data using an example and interpret the background of the analysis results. |
| Class 5 | Intelligent information processing I | Can explain the basics of deep neural network. |
| Class 6 | 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 7 | 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. |
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
【Industrial Engineering and Economics (Macroeconomics and Data Analysis)】
Evaluate the assignment results.
【Communication, Computation, and Intelligent Information Processing】
Evaluate the report.
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.