KAxxx Practical English 1

Hiroshima University Syllabus

Japanese

Academic Year 2020Year School/Graduate School School of Informatics and Data Science

Lecture Code Subject Classification Specialized Education

Subject Name 実用英語 I

Subject Name
（Katakana）ジツヨウエイゴ I

Subject Name in
English Practical English I

Instructor TING HIAN ANN

Instructor
(Katakana) ティンヒェンアン (程賢安)

Campus Higashi-Hiroshima Semester/Term 3rd-Year,  First Semester,  Term 1

Days, Periods, and Classrooms Wednesday 1-4： Engineering 219

Lesson Style Lecture and tutorial 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. During the tutorial, students work in groups to solve problems and submit a group report in English and typset with latex on their laptops.

Credits 2 Class Hours/Week 3 Language of Instruction English

Course Level 1 : Undergraduate Introductory

Course Area（Area） 25 : Science and Technology

Course Area（Discipline） 01 : Mathematics/Statistics

Eligible Students 1st degree undergraduate student

Keywords matrix, determinant, linear transformation, eigenvalue, eigenvector

Special Subject for Teacher Education 　 Special Subject 　

Class Status
within Educational
Program
Criterion referenced
Evaluation Program of Electrical,Systems and Information Engineering
（Abilities and Skills）
・Concepts, knowledge and methods which are the basis for studies related to electrical,  systems, and information engineering.
・Concepts, knowledge and methods which are the basis for studies related to electrical, systems, and information engineering.

Informatics and Data Science Program
（Knowledge and Understanding）
・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.

（Abilities and Skills）
・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.

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 lesson 1 Vector and matrix: An introduction
a. motivation: Why learn linear algebra?
b. matrix and vector
c. matrix algebra
d. square, power, symmetric, skew, and triangular matrices
e. regular and inverse matrices
f. partition of matrix
lesson 2 Tutorial
lesson 3 System of linear equations
a. motivation: What are the applications?
b. system of linear equations and augmented matrix
c. basic transformation and basic matrices
d. echelon and rank
e. Gaussian elimination
lesson 4 Tutorial
lesson 5 Determinant
a. motivation: How important is determinant?
b. permutation
c. basic properties of determinant
d. expansion of determinant; minor and cofactor
e. determinant of special matrices
lesson 6 Tutorial
lesson 7 Linear space
a. motivation: Why study linear spaces?
b. geometric vectors and inner product
c. field; linear (vector) space; subspace
d. linear combination, independence, dependence
e. basis and dimension
lesson 8 Tutorial
lesson 9 Linear mapping
a. motivation: Why study linear mapping?
b. surjective, injective, bijective mapping
c. identity, composite, inverse mapping
d. linear mapping, image, kernel
e. representation matrix, different bases
lesson 10 Tutorial
lesson 11 Inner product space
a. motivation: Why study inner product space?
b. inner product, norm, Schwarz's inequality, triangular inequality
c. orthogonal and orthonormal Bases
d. the Gram-Schmidt Process
e. orthogonal matrix and transformation
lesson 12 Tutorial
lesson 13 Eigenvalue and eigenvector
a. motivation: Principal component analysis for machine learning
b. eigenvalue, eigenvector, and eigen polynomial
c. eigenvalues and eigenvectors of a linear transformation
d. properties of eigenvalues
e. diagonalization and triangularization
lesson 14 Tutorial
lesson 15 Students' presentations

Text/Reference
Books,etc. 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
Class,etc. Slides in the pdf format, which are written with latex's beamer, will be projected, students' presentations

Suggestions on
Preparation and
Review Recommended to revise the contents after each class because they are linearly connected.

Requirements Students need to bring their own laptops for tutorials.

Grading Method The credit will be evaluated based on group work 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) communication skill, (v) group presentation

Message

Other

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.

Academic Year	2020Year	School/Graduate School	School of Informatics and Data Science
Lecture Code		Subject Classification	Specialized Education
Subject Name	実用英語 I
Subject Name （Katakana）	ジツヨウエイゴ I
Subject Name in English	Practical English I
Instructor	TING HIAN ANN
Instructor (Katakana)	ティンヒェンアン (程賢安)
Campus	Higashi-Hiroshima	Semester/Term	3rd-Year, First Semester, Term 1
Days, Periods, and Classrooms	Wednesday 1-4： Engineering 219
Lesson Style	Lecture and tutorial	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. During the tutorial, students work in groups to solve problems and submit a group report in English and typset with latex on their laptops.
Credits	2	Class Hours/Week	3	Language of Instruction	English
Course Level	1 : Undergraduate Introductory
Course Area（Area）	25 : Science and Technology
Course Area（Discipline）	01 : Mathematics/Statistics
Eligible Students	1st degree undergraduate student
Keywords	matrix, determinant, linear transformation, eigenvalue, eigenvector
Special Subject for Teacher Education		Special Subject
Class Status within Educational Program
Criterion referenced Evaluation	Program of Electrical,Systems and Information Engineering （Abilities and Skills）・Concepts, knowledge and methods which are the basis for studies related to electrical, systems, and information engineering. ・Concepts, knowledge and methods which are the basis for studies related to electrical, systems, and information engineering. Informatics and Data Science Program （Knowledge and Understanding）・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. （Abilities and Skills）・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.
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	lesson 1 Vector and matrix: An introduction a. motivation: Why learn linear algebra? b. matrix and vector c. matrix algebra d. square, power, symmetric, skew, and triangular matrices e. regular and inverse matrices f. partition of matrix lesson 2 Tutorial lesson 3 System of linear equations a. motivation: What are the applications? b. system of linear equations and augmented matrix c. basic transformation and basic matrices d. echelon and rank e. Gaussian elimination lesson 4 Tutorial lesson 5 Determinant a. motivation: How important is determinant? b. permutation c. basic properties of determinant d. expansion of determinant; minor and cofactor e. determinant of special matrices lesson 6 Tutorial lesson 7 Linear space a. motivation: Why study linear spaces? b. geometric vectors and inner product c. field; linear (vector) space; subspace d. linear combination, independence, dependence e. basis and dimension lesson 8 Tutorial lesson 9 Linear mapping a. motivation: Why study linear mapping? b. surjective, injective, bijective mapping c. identity, composite, inverse mapping d. linear mapping, image, kernel e. representation matrix, different bases lesson 10 Tutorial lesson 11 Inner product space a. motivation: Why study inner product space? b. inner product, norm, Schwarz's inequality, triangular inequality c. orthogonal and orthonormal Bases d. the Gram-Schmidt Process e. orthogonal matrix and transformation lesson 12 Tutorial lesson 13 Eigenvalue and eigenvector a. motivation: Principal component analysis for machine learning b. eigenvalue, eigenvector, and eigen polynomial c. eigenvalues and eigenvectors of a linear transformation d. properties of eigenvalues e. diagonalization and triangularization lesson 14 Tutorial lesson 15 Students' presentations
Text/Reference Books,etc.	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 Class,etc.	Slides in the pdf format, which are written with latex's beamer, will be projected, students' presentations
Suggestions on Preparation and Review	Recommended to revise the contents after each class because they are linearly connected.
Requirements	Students need to bring their own laptops for tutorials.
Grading Method	The credit will be evaluated based on group work 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) communication skill, (v) group presentation
Message
Other
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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