Hiroshima University Syllabus

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Academic Year 2022Year School/Graduate School School of Informatics and Data Science
Lecture Code KA129001 Subject Classification Specialized Education
Subject Name 実用英語I
Subject Name
Subject Name in
Practical English I
Instructor TING HIAN ANN
ティン ヒェン アン
Campus Higashi-Hiroshima Semester/Term 3rd-Year,  First Semester,  1Term
Days, Periods, and Classrooms (1T) Weds1-4
Lesson Style Seminar Lesson Style
(More Details)
Each lecture is followed by a tutorial as a set of two lessons. Lectures are exclusively in English. There will be simple quizzes and exercises to keep students alert. Each week, before 1 PM, the in-class exercises must be submitted.  Solutions for each homework assignment are to be typeset in English with latex and submit as a pair of latex source file and the resulting pdf file. 
Credits 1.0 Class Hours/Week   Language of Instruction E : English
Course Level 3 : Undergraduate High-Intermediate
Course Area(Area) 25 : Science and Technology
Course Area(Discipline) 01 : Mathematics/Statistics
Eligible Students
Special Subject for Teacher Education   Special Subject  
Class Status
within Educational
・D1. Knowledge and skills required for understanding the theoretical system of statistics and data analysis, and for precisely and efficiently analyzing qualitative/quantitative information in big data.
・A. Skills related to the development of an information infrastructure, information processing techniques, and technology for producing new added value through data analysis.
・B. Ability to identify and solve new problems on their own by quantitative and logical thinking based on data, diverse perspectives, and advanced skills for information processing and analysis. 
Criterion referenced
Informatics and Data Science Program
(Comprehensive Abilities)
・C2. Skills for communication, reading, and writing in English, capabilities required for giving a good, clear oral presentation, and documentation and communication skills that contribute to active discussion.
Class Objectives
/Class Outline
Rapid globalization brings about a need to develop a practical proficiency in English that is applicable internationally. As a foundation to attain the required English proficiency, this course is about learning a wide range of vocabulary so as to be able to read and comprehend, in English, textbooks, articles, and manuals related to informatics and data science. The ability to express such academic contents in English will be developed. Moreover, the basic communication capability for various areas related to informatics and data science will be cultivated. This course will also be beneficial to students in helping them to acquire the ability to learn English on their own.   
Class Schedule lesson1 Vector and matrix: An introduction
lesson2 Tutorial (Exercise with LaTeX)
lesson3 Simultaneous system of first-order equations
lesson4 Tutorial (Exercise with LaTeX)
lesson5 Permutation and determinant
lesson6 Tutorial (Exercise with LaTeX)
lesson7 Linear space
lesson8 Tutorial (Exercise with LaTeX)
lesson9 Linear mapping
lesson10 Tutorial (Exercise with LaTeX)
lesson11 Inner product space
lesson12 Tutorial (Exercise with LaTeX)
lesson13 Eigenvalue and eigenvector
lesson14 Tutorial (Exercise with LaTeX)
lesson15 Summary

Each student's performance is evaluated based on the in-class exercises and homework assignment reports. No examination will be conducted.

Students will learn to apply linear algebra to appreciate machine learning. 
Online lecture notes
線形代数学 (linear algebra) by 栗田 (Kurita), 飯間 (Iima), and 河村 (Kawamura); edited by 久保 (kubo), 2017 
PC or AV used in
(More Details)  
Learning techniques to be incorporated  
Suggestions on
Preparation and
It is important to take notes during the lesson.  
Requirements Lecture notes and teaching materials will be uploaded to bb9 before the lesson begins.  It is important to go through the lecture notes and  homework questions before attending the weekly class. 
Grading Method The credit will be evaluated based on individual reports and presentation. 60/100 point is the minimum requirement. The evaluation is based on (i) fundamental understanding of linear algebra in English, (ii) problem solving skill, (iii) English proficiency demonstrated in the report, (iv) logical steps that arrive at the solution, (v) aesthetic of the report.  

Practical Experience  
Summary of Practical Experience and Class Contents based on it  
Please fill in the class improvement questionnaire which is carried out on all classes.
Instructors will reflect on your feedback and utilize the information for improving their teaching. 
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