2020 Engineering Literacy II g

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Academic unit or major
School of Engineering
Shinozaki Takahiro  Nakahara Hiroki  Matsumoto Ryutaroh  Takahashi Atsushi  Hara Yuko  Nagai Takehiro  Yu Jaehoon  Yoshimura Natsue  Kamigaito Hidetaka  Li Dongju  Aoki Hirotaka  Shioura Akiyoshi  Kurabayashi Daisuke 
Course component(s)
Lecture / Exercise
Mode of instruction
Day/Period(Room No.)
Intensive 5-7(<前半>, 南2号館3階VLSI設計室, <後半>, 西9号館3階311号室)  
Course number
Academic year
Offered quarter
Syllabus updated
Lecture notes updated
Language used
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.
【Manufacturing process 】
【Mechanical Design (Steamship)】
【Mechanical Design (CAD)】
【Wireless electric car with microcomputer】
【Communication, Computation, and Intelligent Information Processing】
【Management Technology (Industrial Engineering and Economics)】

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.
【Management Technology (Industrial Engineering and Economics)】
1) understand the outline of Industrial Engineering (IE) techniques.
2) apply IE techniques to simple cases.
3) understand fundamental problems and algorithms in mathematical optimization.
4) understand fundamental problems and their algorithms in game theory.


【Communication, Computation, and Intelligent Information Processing】
Deep learning, neural network, intelligent information processing, parallel computing, communication
【Management Technology (Industrial Engineering and Economics)】
Management Technology, Industrial Engineering, Cognitive Engineering, Mathematical Optimization, Game Theory

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.
【Management Technology (Industrial Engineering and Economics)】
The lecture explains an outline of the Industrial Engineering techniques and their examples (Cognitive Engineering, Mathematical Optimization, and Game Theory).

Course schedule/Required learning

  Course schedule Required learning
Class 1 Introduction Conduct class interview. Learn how to use VLSI design room. Learn outline of Industrial Engineering techniques.
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 computer through the Internet
Class 4 Intelligent Information Processing III (Train and evaluate deep neural network) Can run a script to train and evaluate a neural network and can investigate a script and explain how the training and evaluation of the neural network is implemented
Class 5 Industrial Engineering Techniques 1(Cognitive task analysis) Carry out an evaluation of interface by applying cognitive task analysis technique.
Class 6 Industrial Engineering Techniques 2 (Mathematical Optimization) Understand fundamental problems and algorithms in mathematical optimization.
Class 7 Industrial Engineering Techniques 3 (Game Theory) Understand fundamental problems and algorithms in game theory.


None required

Reference books, course materials, etc.

Documents will be distributed

Assessment criteria and methods

【Communication, Computation, and Intelligent Information Processing】
Evaluate the report.
【Management Technology (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 (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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