2024年度 シラバス学部

ソフトウェア・エンジニアリング関連科目

2024/11/09  現在

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開講学期
／Semester		 2024年度／Academic Year 　2学期 ／Second Quarter
対象学年
／Course for;		 3年
単位数
／Credits		 3.0
責任者
／Coordinator		 イエン　ニール ユーウェン
担当教員名
／Instructor		 イエン　ニール ユーウェン, 山田　竜平
推奨トラック
／Recommended track		 －
先修科目
／Essential courses		 －
更新日／Last updated on 2024/01/26
授業の概要
／Course outline		 Nowadays, the Web is not only the source of information for the end users. Companies migrate more of their business activities to Web based systems. We are facing increasing demands for professionals who can design large Web systems. Web engineering is a relatively new term in computer science. It can be defined as a discipline of systematic development of Web applications.
授業の目的と到達目標
／Objectives and attainment
goals		 [Corresponding Learning Outcomes]
(A)Graduates are aware of their professional and ethical responsibilities as an engineer, and are able to analyze societal requirements, and set, solve, and evaluate technical problems using information science technologies in society.

The aim of this course is to study current concepts and methods for Web application engineering.
授業スケジュール
／Class schedule		 Topic 1: Introduction
Topic 2: A Web engineering process
Topic 3: Communication and planning
Topic 4: Web App Architectures
Topic 5: Introduction to WordPress
Topic 6: Responsive Web Design
Topic 7: Universal Design for Web
Topic 8: About the Future
Topic 9: Presentation and Discussions

Notes:
** Some topics may need more than 1 time of lectures/exercises to complete. Any updates will be posted on LMS when the class begins.

** Schedule may be adjusted according to actual conditions. Any updates will be posted on LMS when the class begins.
教科書
／Textbook(s)		 No designated textbook is required but students are encouraged to follow the content of

The Modern Web: Multi-Device Web Development with HTML5, CSS3, and JavaScript by Peter Gasston, No Starch Press, 2013.
Dart 1 for Everyone Fast, Flexible, Structured Code for the Modern Web by Chris Strom, The Pragmatic Programmers, LLC., 2014.
Anatomy of a web application using node.js, ExpressJS, MongoDB & Backbone.js by Jason Crol, 2015.
Knockout.js: Building Dynamic Client-Side Web Applications by Jamine Munro, O’Reilly Media, 2015.
成績評価の方法・基準
／Grading method/criteria		 The final grade will be calculated based on the following contributions:
Exercises - 55%
Presentations/Reports - 15%
Quiz - 20%
Active Participation during lectures - 10%

Please note that the proportion to each part may be adjusted according to actual conditions.


コンピテンシーコード表を開く 科目一覧へ戻る

開講学期
／Semester		 2024年度／Academic Year 　4学期 ／Fourth Quarter
対象学年
／Course for;		 3年
単位数
／Credits		 3.0
責任者
／Coordinator		 渡部　有隆
担当教員名
／Instructor		 渡部　有隆, サクセナ　ディーピカー
推奨トラック
／Recommended track		 －
先修科目
／Essential courses		 －
更新日／Last updated on 2024/01/26
授業の概要
／Course outline		 This course covers many current topics of interest in software engineering. Some of the topics covered are formal methods to specify requirements of software systems, software reuse, software maintenance, software maintenance models, and evaluation of processes, products, and resources. It includes Advanced Treatment of Selected Software Engineering issues: Software Maintenance, Software Configuration Management, Software Re-engineering, Managing People, Critical Systems Development, User Interface Design and Evaluation, Emerging Technologies like Visual Programming, Security Engineering, and other advanced topics including student engineering topic.
授業の目的と到達目標
／Objectives and attainment
goals		 [Corresponding Learning Outcomes]
(A)Graduates are aware of their professional and ethical responsibilities as an engineer, and are able to analyze societal requirements, and set, solve, and evaluate technical problems using information science technologies in society.

[Competency Codes]
C-HI-003, C-SD-005, C-SE-001, C-SE-002

The objective of the course is to impart knowledge to students about methods in software development. The methods range from how to precisely specify software requirements to how to evaluate the methods and their products and required resources. This is achieved in two parts: first, lectures are given on several current topics of interest; second, students are given a programming project to design and implement a system using the Object-Oriented programming paradigm and the version management with a team.
授業スケジュール
／Class schedule		 Lecture 1: The Nature of Software Engineering
Topics to study:
- How did software engineering become a term?
- Is there a good technical solution to software development problems?
- How and why are agile methods considered more people-affirming?
- Compare software engineering with other professions.

Lecture 2: Revisioning Software
Topics to study:
- Collaborative development problems,
- Revision control,
- VCS terminology,
- Collaborative development and conflict resolution.

Lecture 3: The Human Factors in Software Engineering
Topics to study:
- Human Diversity;
- Limits to Thinking;
- Knowledge Modeling;
- Personality Types;
- Human Factors Engineering.

Lecture 4: The Managing People and Team Work
Topics to study:
- Selecting and Motivating Staff
- Ego-less Programming
- Managing Groups
- The People Capability Maturity Model

Lecture 5-6: User Interface Design and Evaluation
Topics to study:
- User Interface Design Principles
- User Interaction Styles
- Information Presentation
- GUI Features
- Message System Features
- System Documentation
- User Interface Design Process
- Interface Evaluation

Lecture 7: Visual Programming Systems
Topics to study:
- Terminology
- Classification and Theory
- A Review of Visual Programming Systems

Lecture 8: Software change: Maintenance and Architectural Evolution
Topics to study:
-Program evolution dynamics
-Software maintenance
-Architectural evolution

Lecture 9: Software re-engineering
Topics to study:
-Source code translation
-Reverse engineering
-Program structure improvement
-Program modularization
-Data re-engineering

Lecture 10: Critical systems development
Topics to study:
-Dependable processes
-Dependable programming
-Fault tolerance
-Fault tolerant architectures

Lecture 11: Software Security Engineering
Topics to study:
-Security concepts
-Security risk management
-Design for security
-System survivability
教科書
／Textbook(s)		 1.Software Engineering, 5-9th editions by Ian Sommerville, publisher: Addision-Wesley
2.Human Aspects of Software Engineering by J.E. Tomayko and O. Hazzan, Charles River Media Inc., 2004
3. User Interface Design and Evaluation by D. Stone, C. Jarrett, M.Woodroffe, Sh. Mincha
4. Lecture notes distributed by the instructor will be developed from materials collected from books, journals and proceedings papers.
成績評価の方法・基準
／Grading method/criteria		 Your final grade includes the following parts
1). Quiz: 10 points
2). Exam: 40 points
3). Project: 50 points
履修上の留意点
／Note for course registration		 It would be good if students have knowledge about basics of Software Engineering, Programming in Java  and C.
参考(授業ホームページ、図書など)
／Reference (course
website, literature, etc.)		 The course coordinator Yutaka Watanobe has practical work experience. He collaborated with Givery Inc. to develop the programming skill check tool and materials which can be useful for personnel assessment, training and education. He has provided a number of problems and related test data as one of main contents. He also has experience in developing large scale software in practical use, such as development environments and educational support systems. Based on his experience, he can teach the basics of Software Engineering.


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開講学期
／Semester		 2024年度／Academic Year 　前期 ／First Semester
対象学年
／Course for;		 4年
単位数
／Credits		 3.0
責任者
／Coordinator		 吉岡　廉太郎
担当教員名
／Instructor		 吉岡　廉太郎, 川口　立喜
推奨トラック
／Recommended track		 －
先修科目
／Essential courses		 事前に学んでおいてほしい科目一覧（下記科目内容の一部ないし全部を既知として授業を進めます）
FU14ソフトウェア工学概論
更新日／Last updated on 2024/01/24
授業の概要
／Course outline		 履修学生でチームを組み、顧客の求めるソフトウェアの開発を行うことを通してソフトウェア工学に対する理解を深める授業です。
授業では、顧客から実際に注文を受け、一つの注文を一つの学生チームが受け持ちます。通常3～10名程度のチームになります。各チームは、顧客の要望を聞き取るヒアリングから始め、開発すべきソフトウェアの要件を決定するところから始めます。その後、設計、開発、テストの各開発工程を実施し、第14回授業内で開発作業の概要および成果物の発表を行い、顧客に納品します。
各チームには産業界から派遣されるコーチが付き開発実務のアドバイスを行います。毎週教員・コーチに進捗を報告するとともに、必要に応じて顧客と打合せを持ちます。開発の各行程は成果物（ドキュメント類）で管理し、各工程を終了する度に提出していきます。顧客も参加する中間レビューと最終レビューでは、作業時間数、成果物の量、質などを定量的に分析して報告を行います。
顧客によって定められた機能と品質を満たし、顧客にとって最良のソフトウェアを提供するため、創造と努力の限りを尽くします。そのため、必要となる新たな知識・技術を身につけたり、なんども修正したりするため、授業外の作業時間は当然発生します。チームを構成する全学生の自主的で主体的な取り組みが必須です。
授業の目的と到達目標
／Objectives and attainment
goals		 [対応する学習・教育到達目標]
(A) 技術者としての専門的･倫理的責任を自覚し、情報科学技術を駆使して社会における要求を分析し、技術的課題を設定･解決･評価することができる

[コンピテンシーコード]
C-GV-001, C-HI-003, C-SD-005, C-SE-001, C-SE-002, C-SP-002, C-SP-004, C-SP-006, C-SP-009, C-SP-0011-1, C-SP-012, C-SP-013, C-SP-014

1. 一定の（要求された）機能と品質を満足するソフトウェアを開発するにあたっての課題に気づき、それらを解決する基本的な手法を学ぶ。
2. リソース（人、物、時間など）が限られた中でソフトウェアを完成するためにプロジェクト管理が必要であることを実感し、ソフトウェア工学で学んだ各工程（要求定義・分析、設計、開発、テスト）の手法を実践する。
3. 異分野の技術にまたがる問題の特定や正確な情報伝達など、現実世界で直面する流動的な環境への迅速な対応の必要性を体験し、それに対応するための手法を学ぶ。
履修学生は、上記1～3を通して、比較的大規模で、実用に耐えうるソフトウェアの開発の課題について理解することができる。
授業スケジュール
／Class schedule		 各回の授業（3コマ）では、
1. 各チームによる進捗報告（各10分）
2. 進捗報告に対するフィードバック
3. その週の作業の進め方と留意点に関する講義
4. チーム毎に顧客との打合せ
を行います。

第1回：プロジェクト立ち上げ, 計画立案
チーム内での役割分担を決め、プロジェクト管理システムの準備、顧客からの注文依頼を聞き、要求定義以降の開発プロセスを実行するためのプロジェクト計画を立案する
第2回：現地調査
　顧客の要求を理解し、課題についての理解を深めるため、現場の調査を行う。（顧客の都合等により時期は前後する）
第3回：要件定義
　課題から要求を正確に把握し、明確な要求として整理し、システム化する範囲を顧客と合意する。
第4回：要件定義
課題から要求を正確に把握し、明確な要求として整理し、システム化する範囲を顧客と合意する。積極的にプロトタイプを作成してイメージの共有とすり合わせを行う。
第5回：要件定義
課題から要求を正確に把握し、明確な要求として整理し、システム化する範囲を顧客と合意する。
第6回：中間レビュー・デモ
顧客が要件定義の成果物を精査し、以降のフェーズに着手してよいかどうかの判断を行う。
第7回：分析
要件定義工程の成果物から、要求仕様を論理的に分析し、実現手段を検討する。
第8回：分析
　要件定義工程の成果物から、要求仕様を論理的に分析し、実現手段を検討する。
第9回：設計
システムに必要な機能やオブジェクトの構造や振る舞いを、実装を考慮して詳細化する。
第10回：設計
システムに必要な機能やオブジェクトの構造や振る舞いを、実装を考慮して詳細化する。
第11回：開発
ソフトウェアのソースコードを作成する。
第12回：開発＆テスト
ソフトウェアのソースコードを作成し、テストを行う。
第13回：テスト
　ソフトウェアのテストを行う。
第14回：最終レビュー
顧客がテスト結果および、詳細設計以降の成果物の内容を精査し、受け入れが妥当か否かを判断する。

※積極的にプロトタイピングを行いながら、チームごとにスケジュール管理と作業を行うので進捗は変わってきますが、中間レビューと最終レビューの日付は変えられません。
教科書
／Textbook(s)		 ※必要に応じて講義資料を配布する
成績評価の方法・基準
／Grading method/criteria		 期末試験は行わず、以下の項目で評価する
1. 中間レビューと最終レビューでの発表25%
2. 成果物（ドキュメントおよびソフトウェア）25%
3. 個人レポート 40%
4. 活動への参加と貢献度 10%
※各学生の貢献度は進捗報告とプロジェクト管理システムで確認する。
※評価には顧客とコーチの意見も取り入れる。
※個人レポートでは、プロジェクトでの活動・貢献内容やソフトウェア工学とプロジェクトマネジメントの知識・理解に関する質問について論じてもらいます。
履修上の留意点
／Note for course registration		 • 次の授業を履修していることを前提とした授業です。
FU14ソフトウェア工学概論
IE03ソフトウェア総合演習Ⅰ
IE04ソフトウェア総合演習Ⅱ
• 3下旬～授業開始前の期間に履修希望者に対して実施する事前面談を必ず受けてください。
• プロジェクトごとに必要とされる知識・技術が異なります。
• 開発に必要な技術・知識（設計、プログラミング等）について、スキルが足りない場合には、時間外の自助努力を求めます。
• 授業およびミーティングへの参加は必須です。無断での欠席、遅刻、途中退席は、やむをえない場合を除いてマイナス評価します。
参考(授業ホームページ、図書など)
／Reference (course
website, literature, etc.)		 https://www.u-aizu.ac.jp/enpit/record/2023_software.html
https://www.u-aizu.ac.jp/enpit/record/2022_software.html
https://www.u-aizu.ac.jp/enpit/record/2021_software.html
https://www.u-aizu.ac.jp/enpit/record/2020_software.html
https://www.u-aizu.ac.jp/enpit/record/2019_software.html
https://www.u-aizu.ac.jp/enpit/record/2018_software.html
https://www.u-aizu.ac.jp/enpit/record/2017_software.html

実務経験有り：企業でソフトウェア開発に従事している技術者が講師として毎回の授業に参加し、学生の開発活動に対するレビュー、評価、アドバイスを行います。
現役のソフトウェア技術者（30年以上の実務経験）とソフトウェア開発に従事した経験豊富な教員が共同で授業を行います。


コンピテンシーコード表を開く 科目一覧へ戻る

開講学期
／Semester		 2024年度／Academic Year 　4学期 ／Fourth Quarter
対象学年
／Course for;		 3年
単位数
／Credits		 3.0
責任者
／Coordinator		 モズゴボイ　マキシム
担当教員名
／Instructor		 モズゴボイ　マキシム
推奨トラック
／Recommended track		 －
先修科目
／Essential courses		 Courses preferred to be learned prior to this course (This course assumes understanding of entire or partial content of the following courses)
PL03 JAVA Programming I
更新日／Last updated on 2024/01/24
授業の概要
／Course outline		 Concurrent programs take advantage of modern multicore and multiprocessor machines to implement algorithms that run concurrently (in parallel) to achieve higher performance and better user experience. Distributed computing brings this idea to the next level, dealing with the systems made up of independent computers, linked by a network.

At the present time, both concurrent and distributed systems are widespread, due to high popularity of multicore machines and computer networks. However, the design and implementation of such systems and corresponding software remains a challenging task. We have to know how to coordinate independent processes to achieve high performance and avoid common pitfalls.

The goal of this course is to introduce the basics of concurrent and distributed systems design and implementation. We will cover a number of classical and modern approaches to this problem, paying special attention to the practical aspects of implementation using Java language.
授業の目的と到達目標
／Objectives and attainment
goals		 [Corresponding Learning Outcomes]
(A)Graduates are aware of their professional and ethical responsibilities as an engineer, and are able to analyze societal requirements, and set, solve, and evaluate technical problems using information science technologies in society.

At the end of the course the student should be able to:
- Understand the key advantages of concurrent and distributed systems and common problems the developers may encounter.
- Know different approaches to concurrent and distributed systems design, their advantages and disadvantages.
- Be able to implement simple concurrent and distributed systems using modern tools.
- Be aware of the historical perspective of the developments in this area, understand modern trends and technologies.
授業スケジュール
／Class schedule		 1. Introduction
2. Basics of Concurrency
3. Synchronizing Processes
4. Introduction to Model Checking and Promela Language
5. Model Checking with SPIN and Linear Temporal Logic
6. From Shared-memory Model to Message Passing
7. Distributed Programming with MPI & Tuple Space Model
8. Types of Distributed Systems
9. Client-Server Programming
10. RMI: Distributed Objects in Java
11. Modern Concurrent Programming in Java
12. OpenMP Technology.
教科書
／Textbook(s)		 - Distributed Systems: Principles and Paradigms by Andrew S. Tanenbaum and Maarten van Steen, Prentice Hall, 2007.

- M. Ben-Ari. Principles of Concurrent and Distributed Programming, 2nd Ed. Addison-Wesley, 2006.

- G. R. Andrews. Foundations of Multithreaded, Parallel, and Distributed Programming. Addision-Wesley, 2000.

- M. L. Liu, Distributed Computing: Principles and Applications, Addison-Wesley, 2004.
成績評価の方法・基準
／Grading method/criteria		 The final grade is based on the following parts:

- Exercises (50% of the final score).
- Two exams (30% of the final score).
- Quizzes on lecture content (20% of the final score).

We keep strict deadlines in this course.
履修上の留意点
／Note for course registration		 The presented course is not an introductory subject. It is intended for students who already have basic experience in programming such as Java Programming, Algorithms and Data Structures, Object-Oriented Programming, Operating Systems.


コンピテンシーコード表を開く 科目一覧へ戻る

開講学期
／Semester		 2024年度／Academic Year 　2学期 ／Second Quarter
対象学年
／Course for;		 3年
単位数
／Credits		 3.0
責任者
／Coordinator		 ラゲ　ウダイ キラン
担当教員名
／Instructor		 ラゲ　ウダイ キラン, 山田　竜平
推奨トラック
／Recommended track		 －
先修科目
／Essential courses		 －
更新日／Last updated on 2024/01/29
授業の概要
／Course outline		 Python is a popular programming language widely used to develop large-scale industrial applications. Moreover, Python is tightly coupled with the Database Management System (DBMS) to generate data frames, which are used extensively in AI and big data fields. In this context, the current course aims to educate the students on Python, DBMS, and data frames.

Briefly, this course covers the following topics: (i) fundamentals of Python (architecture, syntax, variable declaration, and compiling), (ii) Object Oriented Concepts in Python, (iii) python documentation, (iv) introduction to mini-world and Entity-Relation schema, (v) introduction to DBMS architecture and Structured Query Language  (vi) Data frames, and (vii) data processing using data frames.

One period of teaching + two periods of exercise
授業の目的と到達目標
／Objectives and attainment
goals		 [Corresponding Learning Outcomes]
(A)Graduates are aware of their professional and ethical responsibilities as an engineer, and are able to analyze societal requirements, and set, solve, and evaluate technical problems using information science technologies in society.

In this course, the students will learn the following topics:
1. Developing applications in Python
2. Designing models to store voluminous data generated by the real-world applications.
3. Approaches to query the needy data
4. Storing and processing the data using data frames for AI and Big Data tasks.
授業スケジュール
／Class schedule		 Lecture topics:
1. Introduction to Python
2. Object Oriented Programming in Python
3. Classes, Packaging, and Documentation
4. Introduction to Database Management Systems
5. Entity-Relationship Model
6. Structured Query language -1 (Meta-data commands: Create, Alter, Drop)
7. Structured Query language-2 (Data commands: Insert, Update, and Delete)
8. Structured Query language-3 (Data commands: Select)
9. Keys and Indexes
10. Object Database Connectivity (ODBC)
11. Data frames
12. Managing data frames
13. Knowledge discovery in data

Exercise topics:
1. Exercise on input and output operations in Python
2. Tower of Hanoi in Python
3. Reading files into dictionary and searching.
4. Web crawling/scrapping application
5. ER-model and database design for air pollution data
6. Storing the air pollution data in the database
7. Inserting the data into the database
8. Creating the indexes
9. Reading the data from the files and inserting into the database
10. Discussion on previous exercises
11. Generating data frames for SQL
12. Processing air pollution data that exists in data frames
13. Pattern mining on the data frames
教科書
／Textbook(s)		 Database Systems: The Complete Book, Hector Garcia-Molina, Jeffrey Ullman, Jennifer Widom

Python course presentation from Stanford University: http://web.stanford.edu/class/archive/cs/cs106a/cs106a.1212/lectures/4-Variables/4-IntroPython.pdf
成績評価の方法・基準
／Grading method/criteria		 Students will be graded based on the quizzes, exercises, and the final exam.  
Exercise do not carry negative marks. However, quizzes and final exam will carry negative marks.

Grading will be done using Percentile basis. Thus, the formula for grading is as follows:
            (yourTotalMarksInQuizzes+Exercises+FinalExam*100)/maximumMarksGotByStudent
履修上の留意点
／Note for course registration		 It is advisable for the students to have familiarity with the following courses:
a) Operating systems
b) Structured Programing language
参考(授業ホームページ、図書など)
／Reference (course
website, literature, etc.)		 Stanford university: https://web.stanford.edu/class/cs245/


コンピテンシーコード表を開く 科目一覧へ戻る

開講学期
／Semester		 2024年度／Academic Year 　3学期 ／Third Quarter
対象学年
／Course for;		 3年
単位数
／Credits		 3.0
責任者
／Coordinator		 ラゲ　ウダイ キラン
担当教員名
／Instructor		 ラゲ　ウダイ キラン, ダン　ナム　カイン
推奨トラック
／Recommended track		 －
先修科目
／Essential courses		 －
更新日／Last updated on 2024/01/29
授業の概要
／Course outline		 Big data analytics (BDA) represents a complex process of analyzing voluminous data to uncover useful information -- such as hidden patterns, correlations, market trends and customer preferences -- that can help organizations make informed business decisions.

BDA is the fundamental course for machine learning and deep learning courses. Without proper knowledge on BDA, it is very difficult to develop machine learning/deep learning process.

A student/person who aspires to achieve mastery in machine learning/deep learning must have acquire the knowledge on the following courses:

Database systems – Artificial Intelligence – Big Data Analytics – Information Extraction and Retrieval – Graph Analytics – Machine Learning – Deep Learning

One period of teaching + two periods of exercise
授業の目的と到達目標
／Objectives and attainment
goals		 [Corresponding Learning Outcomes]
(A)Graduates are aware of their professional and ethical responsibilities as an engineer, and are able to analyze societal requirements, and set, solve, and evaluate technical problems using information science technologies in society.

Technological advances in the field of computer science have enabled organizations to collect voluminous data in databases. Useful knowledge that can empower end-users with competitive information is hidden in this voluminous data. Unfortunately, conventional statistical techniques are inadequate to extract knowledge hidden in this data. This scenario is called “Data Rich-Information Poor Situation” (or finding a needle in a haystack).  The field of BDA has emerged to address this problem in real-world applications.

In this course, the students will learn the following topics:
1. Properly storing the raw data in the databases.
2. Constructing data warehouses to analyze voluminous data.
3. Various imputation techniques.
4. Different knowledge discovery techniques, such as Pattern mining, Clustering, Classification, and Prediction.
授業スケジュール
／Class schedule		 Lecture topics:
1. Introduction to Online Transaction Processing (OLTP)
2. Introduction to Online Analytical Processing (OLAP)
3. Introduction to Data Mining and ETL (Extract, Transform, and Load) techniques
4. Prediction (Linear and Auto Regression models)
5. Time series forecasting (ARIMA)
6. Classification (Bayesian theorem)
7. Classification (Decision trees)
8. Time series classification
9. Ensemble techniques and Voting procedures
10. Clustering (K-Means)
11. Clustering (DBSCAN)
12. Association Rule Mining
13. Pattern Mining


Exercise topics:
1. Draw the ER-schema, create tables and store the data in PostGres database for the given air pollution data. (OLTP)
2. Construct the data warehouse for the data stored in Exercise-1
3. Perform imputation and fill-up the missing values in the data warehouse created in Exercise-2
4. Performing linear regression and auto regression on the air pollution data
5. Perform ARIMA on the air pollution data
6. Exercise on Bayesian Classification
7. Exercise on Decision trees Classification
8. Time series classification
9. Developing an Ensemble classification model
10. Exercise on KMeans algorithm to cluster air pollution data
11. Exercise on DBSCAN algorithm to cluster pollution data
12. Mining association rules from the air pollution data
13. Mining user interest-based from the air pollution data.
教科書
／Textbook(s)		 Data Warehousing and Mining: Han and Kamber
成績評価の方法・基準
／Grading method/criteria		 Students will be graded based on the quizzes, exercises, and the final exam.  
Exercise do not carry negative marks. However, quizzes and final exam will carry negative marks.

Grading will be done using Percentile basis. Thus, the formula for grading is as follows:
            (yourTotalMarksInQuizzes+Exercises+FinalExam*100)/maximumMarksGotByStudent
履修上の留意点
／Note for course registration		 It is advisable for the students to have familiarity with the following courses:
a) Operating systems
b) Database systems
c) Programing language
参考(授業ホームページ、図書など)
／Reference (course
website, literature, etc.)		 Stanford university: https://web.stanford.edu/class/cs246/index.html#schedule
The University of Tokyo: https://ocwx.ocw.u-tokyo.ac.jp/course_11414/

Data Mining text book soft copy: http://myweb.sabanciuniv.edu/rdehkharghani/files/2016/02/The-Morgan-Kaufmann-Series-in-Data-Management-Systems-Jiawei-Han-Micheline-Kamber-Jian-Pei-Data-Mining.-Concepts-and-Techniques-3rd-Edition-Morgan-Kaufmann-2011.pdf


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