AY 2019 Undergraduate School Course Catalog

Foundations of CSE

2019/04/25

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開講学期
/Semester
2019年度/Academic Year  4学期 /Fourth Quarter
対象学年
/Course for;
1st year
単位数
/Credits
4.0
責任者
/Coordinator
Yutaka Watanobe
担当教員名
/Instructor
Yutaka Watanobe, Yan Pei, Jie Huang, Wenxi Chen, Wanming Chu, Qiangfu Zhao
推奨トラック
/Recommended track
履修規程上の先修条件
/Prerequisites

更新日/Last updated on 2019/02/15
授業の概要
/Course outline
Computers are machines that manipulates information. Fundamental study of computer science includes the problems how information is organized in the computer, how it can be manipulated, and how it can be utilized. The efficiency of programming and data processing is directly linked to algorithms and the structures of the data being processed. Thus, it is crucial for students of computer science to understand the concepts of information organization, information manipulation and algorithms.
In this course, students learn a range of algorithms and data structures as well as how to implement them through programming exercises.
授業の目的と到達目標
/Objectives and attainment
goals
Students will be able to evaluate the strengths and weaknesses of data structures and algorithms as well as to develop efficient algorithms. In addition, the students will be able to apply the state-of-the-art of data structures and algorithms for solving problems and developing software. The aim of the exercises is to solve practical problems under limited resources and know from experience that the efficiency of an algorithm is much more important than that of hardware. Acquired knowledge and techniques will be used for their state-of-the-art research and software development in the future.
授業スケジュール
/Class schedule
1 Introduction
2 Analysis of Algorithms, Sort I
3 Data Structures
4 Search I, Hash
5 Recursion, Divide and Conquer
6 Sorting II
7 Tree
8 Binary Search Tree
9 Heap
10 Dynamic Programming
11 Graph
12 Graph Algorithms
13 Heuristic Search
14 String Matching
教科書
/Textbook(s)
Textbooks
Thomas H. Cormen, Introduction to Algorithms 1.

Supplementary reader
Thomas H. Cormen, Introduction to Algorithms 2.
Y. Watanobe, Algorithms and Data Structures for Programming Contest.
成績評価の方法・基準
/Grading method/criteria
AA: Algorithm Assignment - Max: 100 points
PA: Programming Assignment - Max: 120 points
CE: Coding Examination - Max: 120 points
PE: Paper Examination - Max: 120 points
Theory: sqrt(AA × PE)
Practice: sqrt(PA × CE)
Final Score = sqrt(Theory × Practice)
履修上の留意点
/Note for course registration
Students should learn Intro.Programming and C Programming.

参考(授業ホームページ、図書など)
/Reference (course
website, literature, etc.)
Couse Web Site
http://web-int.u-aizu.ac.jp/course/alg1/

Web Site for exercises
https://onlinejudge.u-aizu.ac.jp/

References
Thomas H. Cormen, Charles E. Leiserson,  Ronald L. Rivest, Clifford Stein, Introduction to Algorithms (MIT Press).
Robert Sedgewick, Algorithms in C, Parts 1-5 (Bundle): Fundamentals, Data Structures, Sorting, Searching, and Graph Algorithms.

The course instructor Yan Pei has practical work experience. He worked for Neusoft Ltd. (China、CMMI5) and Alpine Electronics Europe D&R GmbH (Germany) for five years, where he attended and led software development projects (million LOC level) and teams, designed core algorithms, and applied for and obtained industrial patents. Based on these experiences, he can deliver lectures on algorithm and complexity, programming, and software engineering, etc.

The course coordinator Yutaka Watanobe has practical work experience. Now he is collaborating with Givery Inc. to develop the programming skill check tool 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. Based on his experience, he can teach a wide range of algorithms and data structures.

The course instructor Wenxi Chen has practical working experience. He had worked for Nihon Kohden Industrial Corp. for 5 years where he was involved in R&D of bioinstrumentation, signal processing and data analysis. Based on his experience, he can teach the basis of Algorithms and Data Structures.


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開講学期
/Semester
2019年度/Academic Year  4学期 /Fourth Quarter
対象学年
/Course for;
3rd year
単位数
/Credits
3.0
責任者
/Coordinator
Hirohide Demura
担当教員名
/Instructor
Hirohide Demura, Shigeo Takahashi, Anh T. Pham
推奨トラック
/Recommended track
履修規程上の先修条件
/Prerequisites

更新日/Last updated on 2019/02/17
授業の概要
/Course outline
This course “Information Theory and Data Compression” gives knowledge and basic skills as follows. Transmitting information efficiently and accurately is one of the important technical challenges in the modern digital society. Information theory is rooted in mathematical formulation and provides a theoretical solution to this problem. The idea of information theory makes it possible to construct an efficient coding for information communication and error correction by utilizing the probability and the statistical theorem. Information theory plays an important role in fields such as image data compression, cryptology theory, network communication, information quantity evaluation, etc.
授業の目的と到達目標
/Objectives and attainment
goals
The main contents of this course are mathematical formulation of source coding and channel coding, efficient construction method of coding, and entropy for measuring information ambiguity and information volume. This course includes basis of probability and statistics, and data compression from viewpoints of coding tree and quantization table.
授業スケジュール
/Class schedule
Lecture for Demura’s class
# 1 Guidance, About the information theory
# 2 Conditional Probability and Law of Large Numbers
# 3 Instantaneous code
# 4 Code space
# 5 Source coding theory & Huffman code
# 6 Various source codings
# 7 Entropy
# 8 Mutual information
# 9 Maximum likelihood decoding
# 10 Hamming distance
# 11 Channel Coding Theory
# 12 Hamming code
# 13 Generator matrix
# 14 Primitive polynomial

Lecture for Pham’s class
# 1 Course Introduction
# 2 Introduction to Information Theory
# 3 Errors and Error Detecting Codes
# 4 Error Correcting Codes - Repetition Codes
# 5 Hamming codes
# 6 Probability
# 7 Entropy & Inference
# 8 Source Coding Theorem
# 9 Data Compression
# 10 Huffman (Symbol) Code
# 11 Mutual information
# 12 Channel Capacity
# 13 Channel Coding Theorem
# 14 Information Transmission over Noisy Channel

Exercise for all classes
We have an exercise class (two periods) a week.
Topics are Data compression, Error Detection and Correction based on Communications System Toolbox, MATLAB.
教科書
/Textbook(s)
Demura’s class:
Introduction to information theory based on examples  (in Japanese)
Shinichi Oishi (1993) Kodansha
ISBN 978-4-06-153803-0

Pham’s class:
Information Theory, Pattern Recognition, and Neural Networks (English, free)
David J. C. MacKay, University of Cambridge
ISBN: 9780521642989
成績評価の方法・基準
/Grading method/criteria
Final exam 50%, Quiz(2nd-14th lectures) 10%, Exercise 40%
参考(授業ホームページ、図書など)
/Reference (course
website, literature, etc.)
The course instructors has working experiences: Instructors are familiar with Information Theory and Data Compression based on telecommunication network design, etc.

Communications System Toolbox, MATLAB
https://jp.mathworks.com/help/comm/


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開講学期
/Semester
2019年度/Academic Year  2学期 /Second Quarter
対象学年
/Course for;
2nd year
単位数
/Credits
3.0
責任者
/Coordinator
Kazuyoshi Mori
担当教員名
/Instructor
Masahide Sugiyama, Kazuto Asai, Kazuyoshi Mori, Igor Lubashevskiy, Nobuyoshi Asai, Yodai Watanabe
推奨トラック
/Recommended track
履修規程上の先修条件
/Prerequisites

更新日/Last updated on 2019/02/14
授業の概要
/Course outline
"Discrete Systems (DS)" is called "Discrete Mathematics". This course
deals and analyzes mathematically various discrete events and objects,
and has been developed as mathematical concept and fundamental
methodology for computer and its networks, programming and
algorithms. In that sense, course DS is mathematical foundation for
computer science and technology, and has many fundamental studies and
various applications. In the freshmen of University education, DS is
completely different from "Linear Algebra" and "Calculus". DS is
universal infrastructure and common language to represent modern ICT
(Information and Communication Technology).
授業の目的と到達目標
/Objectives and attainment
goals
As described in "Course Outline", DS is mathematics for Computer
Science and Engineering, and is supporting foundation of modern ICT.
Objectives of this course is that students can understand and
represent various discrete events (phenomena) using DS as a universal
language and infrastructure. Moreover, students are recommended to
implement programs using theories studied in DS.
授業スケジュール
/Class schedule
In Second Quarter (Q2) students will study in the following four
lecture classes and six exercise sessions.

       Sugiyama   Mori         Lubashevsky     K.Asai
       C1,C2      C3,C4        C5,ITCG         C6
       M5         M6           M7              M8
       Mon,Thu    Tue,Fri      Tue,Fri         Tue,Fri
       1,2,3      5,6,7        7,8,9           7,8,9

     Exercise classes and assigned professors (room)

C1: M.Sugiyama (M5)
C2: N.Asai (M6)
C3: K.Mori (M6)
C4: Y.Watanabe(M9)
C5: I.Lubashevsky(M7)
C6: K.Asai(M8)

Contents of lecture and Exercise are constructed 4 Blocks with 14
sessions.

The first block "Basic" contains "Set and Logic", "Functions" and
"Relations", which are fundamentals for further study in
DS. Operations for subsets, set family contains set elements, injection
(one-to-one mapping) and surjection, definition and representation of
binary relation, equivalent relation and set partition. It is necessary
for students to understand and apply this "Basic".

The second block "Counting and Combinatorics" contains basic property
of integers, recursive definition and counting of events and
objects. "Module calculation" covers basic property and calculation of
integer module, "Recursion and Induction" covers principle of
mathematical induction, recursive definition, In "Combinatorics"
covers principles of sum and product, pigeon hole principle,
permutation and combination, various counting techniques.

The third block "Graph theory" contains general idea of graphs and
applications of graphs. Practical events and relations are represented
and understood using concept of graphs. "Basics of Graphs" covers
fundamental notions of graph theory; vertex, edge, face and path,
fundamental properties of graphs, and matrix representation of graphs
for computation implementation. "Directed graphs (DAG)" covers graphs
with direction (orientation) to represent more practical
modeling. "Planar graphs" covers Euler's theorem and polyhedron
theorem. Finally "Trees" covers tree structures as a special case of
graphs.

The fourth block "Order, Logic, Boolean algebra" contains abstract
"Order" relation which is analogy of order property for numbers.
"Lattice" is introduced as a special partially ordered set which has
algebraic structure with two binary operations. "Logic" and "Boolean
algebra" covers elementary logic and propositional calculus. "Boolean
algebra" is a special type of lattice, and related to construction of
switching circuits.

   1. Basic (3 lectures)
     - Set Theory and Logic
     - Functions
     - Relations

   2. Counting and Combinatorics (2-3 lectures)
     - Modulo calculation
     - Recursion and Induction
     - Combinatorics

     Mid-term exam (optional)

   3. Graph theory (4-5 lectures)
     - Basics of Graphs
     - Directed graphs (DAG)
     - Planar graphs
     - Tree

   4. Order, Logic, Boolean algebra (3-4 lectures)
     - Order
     - Lattice
     - Logic
     - Boolean algebra
教科書
/Textbook(s)
  Prof. M.Sugiyama's class
    S.Lipschutz, Theory and Problems of Discrete Mathematics, Ohmsha.
    
  Prof. K.Mori's class
    Texts not specified.  Necessary materials will be distributed in the
    class.

  Prof. I.Lubashevsky's class

  Prof. K.Asai's class
    Handouts delivered
成績評価の方法・基準
/Grading method/criteria
  Doing or not doing mid-term exam and grading methods are different
  professor by professor. If you have any questions, please contact to
  your class professor.

  The followings are current grading methods of each class.

  Prof. M.Sugiyama's class
     Exams (70%), [Mid-exam(30%),Final-exam(40%)], Exercise (30%)

  Prof. K.Mori's class
     Midterm exam 30%, Final exam 40%, Exercises 30%

  Prof. I.Lubashevsky's class
     Midterm exam 30%, Final-exam 40%, Home work 30%
     
  Prof. K.Asai's class
    By Final Exam. and Exercise. (More than 80% of homework
    assignments should be submitted.) Full score of Final Exam. is
    approx. 150 points. The raw score "p" is converted to a scaled score
    "s" by the formula: s=75+(p-75)/3+e (p>75), s=p+e (p≦75) (in
    principle). Here, "e" is Exercise score, which is added up to s=80.
履修上の留意点
/Note for course registration
  Doing or not doing mid-term exam and grading methods are different
  professor by professor.
参考(授業ホームページ、図書など)
/Reference (course
website, literature, etc.)
  Prof. M.Sugiyama's class
    http://web-int.u-aizu.ac.jp/~sugiyama/Lecture/DS/2017/welcome.html

  Prof. K.Mori's class

    Seymour Lipschutz, Hiroshi Narushima, "Discrete Mathematics",
    Ohmsha. translated version (Revised Third Edition)

    Seymour Lipschutz, Marc Lipson, "Discrete Mathematics", Series of
    Schaum's Outline Series, Fundamental Mathematics in Computer
    Science, McGraw Hill.

  Prof. I.Lubashevsky's class

  Prof. K.Asai's class
    Directory for Asai's class: ~k-asai/classes/disc/
    Handouts and Exercises for Asai's class:
    http://web-ext.u-aizu.ac.jp/~k-asai/classes/class-texts.html

  The course instructors have working  experiences:


   - M.Sugiyama
1979-1986 NTT Musashino ECL (NTT Musashino Research Center)
1986-1987 AT&T Bell Lab. visiting researcher
1987-1990 NTT Basic Research Lab.
1990-1993 ATR Interpreting Telephony Research Lab.   
"Counting and Combinatorics" is related research
on information retrieval.

   - K.Mori
1988-1988 Shinko Electric Co.,Ltd.
      Implementataion of Computer Algebra System, REDUCE, on SELCOS, a
      computer system for Industrial Control.

   - N.Asai
1997-2000 Researcher, WaveFront Co. Ltd.
2002-2003 Visiting researcher, National Institute of Environmental Study
2001-2010 Collaborative Research with Asahi Glass Company

He has practical work experience at Wave Front Co. Ltd for
numerical simulations, modelings and high performance
computings from 1997 to 2000. After joined to U. of Aizu, he
has continued with companies on the mentioned topics.  These
experiences of designing models, data structure and algorithms
are deeply related with this class topics, or each of this
class topics is important basic element for practical design.


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開講学期
/Semester
2019年度/Academic Year  3学期 /Third Quarter
対象学年
/Course for;
2nd year
単位数
/Credits
4.0
責任者
/Coordinator
Hiroshi Saito
担当教員名
/Instructor
Wanming Chu, Satoshi Nishimura, Hiroshi Saito, Yoichi Tomioka, Yuichi Okuyama, Yukihide Kohira
推奨トラック
/Recommended track
履修規程上の先修条件
/Prerequisites

更新日/Last updated on 2019/03/29
授業の概要
/Course outline
Logic circuit design is a design process in digital VLSIs such as processors. The main objective of logic circuit design is to design functional requirements implemented on digital VLSIs using logic variables which take 0 or 1 and logic operations (AND, OR, NOT). Not only circuits are designed from a specification correctly but also it is necessary to design optimum circuits to satisfy design requirements such as cost and performance.
授業の目的と到達目標
/Objectives and attainment
goals
In lectures, students study the basic knowledge, design method, and optimization method
for logic circuit design. In exercises, students design logic circuits from specification
such as truth table using a schematic editor. In addition, students verify whether the
designed circuits are correct or not by using a logic simulator.

The goals of this course are as follows:
1. Students can design the minterm cannonical disjunctive form from truth tables
2. Students can minimize two-level logic functions using Karnaugh map
3. Students can design sequential circuits using finite state machine
授業スケジュール
/Class schedule
1. Introduction
2. Representation of numbers
3. Boolean algebra
4. Two-level logic minimization using Karnaugh map
5. Various representations of logic functions
6. Delay and performance of logic circuits.
7. Mid-term examination
8. Combinational circuits 1
9. Combinational circuits 2
10. Memory logics
11. Design of sequential circuits
12. Design of sequential circuits using finite state machine
13. Summary
14. Others
教科書
/Textbook(s)
Not assigned
成績評価の方法・基準
/Grading method/criteria
Mid-term/final examination 60%
Reports 40%

No re-examination
履修上の留意点
/Note for course registration
The course instructor Yuichi Okuyama has practical working experience.
He worked for NTT labs where he was involved in R&D of semiconductor integrated circuits and EDA tools.
Based on his experience, he can teach the basics of logic circuit design.


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開講学期
/Semester
2019年度/Academic Year  1学期 /First Quarter
対象学年
/Course for;
3rd year
単位数
/Credits
4.0
責任者
/Coordinator
Toshiaki Miyazaki
担当教員名
/Instructor
Toshiaki Miyazaki, Wanming Chu, Satoshi Nishimura, Naohito Nakasato, Abderazek Ben Abdallah
推奨トラック
/Recommended track
履修規程上の先修条件
/Prerequisites

更新日/Last updated on 2019/02/18
授業の概要
/Course outline
Students will learn fundamental issues of computer architecture with its design approach, and performance evaluation methods. A computer consists of a central processing unit, memory, I/O devices, and so on. In this course, the students will understand how a computer is established by combining some computing and control units which are composed of logic circuits learned in “Logic Circuit Design” course. More precisely, the students will learn abovementioned issues using MIPS processor as an example. In the exercises, the students will implement a simplified MIPS processor by using CAD (Computer Aided Design) tools such as Cadence. In addition, the students will study how a program runs on a processor through developing some programs using an assembly programming language.
授業の目的と到達目標
/Objectives and attainment
goals
1. Understand the main principles of computer architecture.
2. Learn the fundamental of assembly programming.
3. Use CAD tools and a hardware description language, and learn the abstract of processor design.
授業スケジュール
/Class schedule
Lecture schedule / contents
1. Introduction (Chapter 1)
2. Performance evaluation (Chapter 1)
3. Instruction set & assembly language (Chapter 2)
4. Instruction set & assembly language 2 (Chapter 2)
5. Arithmetic for computer: addition, subtraction, ALU, etc (Chapter 3)
6. Arithmetic for computer2: multiplication, division, floating point (Chapter 3)
7. Datapath (Chapter 4)
8. Controller (Chapter 4)
9. Pipeline (Chapter4)
10. Pipeline 2: hazards (Chapter 4)
11. Memory hierarchy: cache (Chapter 5)
12. Memory hierarchy: virtual memory (Chapter 5)
13. Storage & other I/Os:, RAID, etc (Chapter 5 +α)
14. Parallel processor: SIMD/MIMD, Vector processors, etc (Chapter 6)

Exercise schedule / contents
1. Introduction
2. Assembly Programming Language (Basic instructions only)
3. Assembly Programming Language (Develop a multiply routine using immediate instructions)
4. Assembly Programming Language (JAL and sub-routine call)
5. Understand mechanisms of RF and ALU(Confirm using a simulator)
6. Develop a simple CPU and confirm its behavior using basic instructions
7. Develop a simple CPU and confirm its behavior using the multiply routine developed in Ex. 3
8. Develop a simple CPU and confirm its behavior using the program calling sub-routines, which is developed in Ex. 4
9. Reserve day for catching up delay of Ex 5-7, and review
10. Pipeline (Solve ex. problems in the textbook)
11. Cache (Solve ex. problems in the textbook)
12. Virtual memory (Solve ex. problems in the textbook)
13. Integrated final exercise 1 (or Ex for multicore, or review of previous exercises. Depending on the progress of the class)
14. Integrated final exercise 2 (or Ex for multicore, or review of previous exercises. Depending on the progress of the class)
教科書
/Textbook(s)
Computer Organization and Design - The Hardware/Software Interface. David. A. Patterson and John L. Hennessy, 5th edition, Morgan Kaufmann Publishers, ISBN 0124077269
成績評価の方法・基準
/Grading method/criteria
Final examination (50%), Exercise report (50%). (No makeup examination)
履修上の留意点
/Note for course registration
(Ben class) The lecture will be offered in English

Related courses which include important concepts relevant to the course
Courses should be taken before taking this course:
-Introduction to Computer Systems
-Logic circuit design

Related courses
-Operating Systems
-Parallel Computer Architecture
-Embedded Systems
参考(授業ホームページ、図書など)
/Reference (course
website, literature, etc.)
Course website:
Will be announced in the first class

Related literature
“HDLによるVLSI設計―VerilogHDLとVHDLによるCPU設計 第2版” 深山正幸 他著 (in Japanese)

----
The course instructor (Miyazaki) has practical working experience. He worked for NTT labs for 22 years where he was involved in R&D related to telecommunication systems using CPUs and FPGAs and computer-aided design methodologies dedicated to them. Based on his experience, he can teach the basics of computer architecture.


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開講学期
/Semester
2019年度/Academic Year  4学期 /Fourth Quarter
対象学年
/Course for;
2nd year
単位数
/Credits
4.0
責任者
/Coordinator
Alexander P. Vazhenin
担当教員名
/Instructor
Alexander P. Vazhenin, Yong Liu, Hitoshi Oi, Konstantin Markov, Yohei Nishidate, Peng Li, Kazuya Matsumoto
推奨トラック
/Recommended track
履修規程上の先修条件
/Prerequisites

更新日/Last updated on 2019/02/14
授業の概要
/Course outline
Operating systems are an essential part of any computer system. Similarly, a course on operating systems is an essential part of any computer-science education. The operating system provides certain services to programs and to the users of those programs in order to make the programming task easier. In this course, four parts of the core of operating systems will be mainly learned. They are process management, memory and storage management, file system management, and I/O system management. Also, some abstract concepts such as cooperation of multi-process and deadlock will be discussed. We do not concentrate on any particular operating system or hardware. Instead, we discuss fundamental concepts that are applicable to a variety of systems. We present a large number of examples that pertain specifically to UNIX and to other popular operating systems.
授業の目的と到達目標
/Objectives and attainment
goals
1. To learn the concepts about OS.
2. To get the knowledge about process management, including process concept, inter-process communication, and CPU scheduling.
3. To learn memory management including page management and virtual memory.
4. To learn the design and implementation of file system.
5. To get the knowledge about software and hardware of I/O system.
授業スケジュール
/Class schedule
1. Operating System Concepts and Components
Topics to study:
OS Concepts
Computer-system components
Evolution Steps
OS Components
OS Services

2. PROCESSES
Topics to study:
Process Concept
Process States and Scheduling
Operations on Processes
Cooperating Processes
Threads
Inter-process Communication

3. CPU Scheduling
Topics to study:
Basic Concepts
Scheduling Criteria
Scheduling Algorithms
Algorithm Evaluation
Conclusion

4-5. Process Synchronization and Deadlocks
Topics to study:
Background
Critical Section Problem
Semaphores
Classical Problems
Necessary Conditions of Deadlocks
Resource Allocation Graph
Deadlock Prevention
Deadlock Avoidance
Deadlock Detection
Recovery from a Deadlock

5-9. Memory Management
Topics to study:
Background
Logical versus Physical Address Space
Swapping
Contiguous Allocation
Paging Model of Logical and Physical Memory
Implementation of a Page Table
Multilevel Paging
Inverted Page Table
Segmentation: Basic Methods
Segmentation with Paging
Demand Paging
Performance of Demand Paging
Page Replacement Algorithms
Allocation Frames
Thrashing
Demand Segmentations

10-11. File Management
Topics to study:
File-System Concepts
File Attributes
File Operations and Access Methods
Directory Structure and Implementation
Protection Mechanizm
File-System Organization
Allocation Methods
Free-Space Management
Directory Implementation
Efficiency and Reliability
Mass-Storage Management

12-13. Distributed Systems
Topics to study:
Background
Motivation
Topology
Network Types
Communication Strategies
Design Strategies

14. Mass Storage Structure
Disk structure, attachment, scheduling, management.
Swap space management.
RAID structure and implementation
Operating system and performance issues.

教科書
/Textbook(s)
1. Modern Operating Systems, by Andrew S. Tanenbaum, Prentice-Hall, Inc.
2. Operating System Concepts, 5-9, A. Silberschatz and P. B. Galvin, John Wiley & Sons, Inc.
3. Materials and handouts provided by instructors
成績評価の方法・基準
/Grading method/criteria
This policy is used by all course instructors.
Final examination, midterm, experimental reports.
Exercises: 40 points
Quiz, Midterm + Exam: 60 points

Reports about results of exercises should be submitted about one week after each exercise.  
履修上の留意点
/Note for course registration
Computer Architecture or Computer Organization, Basic Algorithms and Data Structures, Programming I (C or C++) or Java

Formal prerequisites:L4 Intro. Computer Systems
参考(授業ホームページ、図書など)
/Reference (course
website, literature, etc.)
The course instructor Alexander Vazhenin has practical working experience. He worked for the Computer Center of Siberian Division of the Russian Academy of Sciences for 15 years where he was involved in R&D of software design and operating systems. Based on his experience, he can teach the basics of Operating Systems.
The course instructor Hitoshi Oi has  more than five years of industrial experiences at Digital Equipment Corporation, ASTEM RI/Kyoto and HAL Computer
Systems.

1. Course WWW-site: http://sealpv2u-aizu.ac.jp
2. 「オペレーティングシステム」前川 守著 岩波書店 ISBN4000103466


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開講学期
/Semester
2019年度/Academic Year  4学期 /Fourth Quarter
対象学年
/Course for;
2nd year
単位数
/Credits
3.0
責任者
/Coordinator
Taro Suzuki
担当教員名
/Instructor
Taro Suzuki, Mohamed Hamada, Kazuyoshi Mori
推奨トラック
/Recommended track
履修規程上の先修条件
/Prerequisites

更新日/Last updated on 2019/02/14
授業の概要
/Course outline
Theory of automata and languages is one of the most fundamental fields of theory of computing. The core concept is to obtain the method to describe infinite sets which are called languages (countable ones are dealt in this field mainly).

We study two important systems to describe them: Automata, the recognizing/accepting systems, and Grammars, the generating systems. We study the relation of formal languages through grammars and automata. We focus on hierarchies of languages.

They are standard tools of the well-educated computer scientist, who often uses them to reformulate a problem in an easily solvable form, or to recognize the language that cannot be feasibly handled in general.
授業の目的と到達目標
/Objectives and attainment
goals
At the end of the course the student should be able to:

1. know the necessity to describe infinite (countable)
   sets(= languages) correctly

2. know methods to describe languages, automata as recognizers
   and grammars as generators of them

3. design automata and grammars for languages

4. understand the restriction on the automata and grammars and
   the hierarchy of describing powers of languages caused by
    such restriction

5. understand the relation between automata and grammars
授業スケジュール
/Class schedule
Although class schedule depends on professors, each professor deals with the following topics (the order depends on professors).

1. Introduction

Important items students should know and obey are explained. Then, languages we study in this course is introduced, and automata and grammars as devices for describing languages are generally explained.

2. Automata

Automata and languages accepted by them are extensively studied. We deal with finite automata(FA) and pushdown automata(PDA). Three classes of finite automata (deterministic finite automata(DFA), nondeterministic finite automata(NFA), nondeterministic finite automata with empty moves(ホサ-NFA, also known as ホオ-NFA)) and their relationship is explained. We also deal with minimization of DFAs.

3. Grammars

Grammars and languages generated by them are explained in detail. Following four types of grammars and languages are explained: regular grammars(RG) and regular languages(RL), context-free grammars(CFG) and context-free languages(CFL). We deal with regular expressions(RE) as an expression of regular languages. Normal forms of context-free grammar (Chomsky normal form(CNF) and Griebach Normal form(GNF) are also explained.

4. Relationship between Automata and Grammars

We learn relationship between automata and grammars as the devices to describe languages. Correspondences between finite automata and regular grammars, pushdown automata and context-free grammars are explained,

5. The Hierarchy of Language Classes

We establish a hierarchy among language classes and show some property which every language in one class satisfies, and then using it, we show the existence of a language which does not belong to the class. That is, we explain followings: Properties of Regular Languages and the Existence of Languages which are not Regular, Properties of Context-Free Languages and the Existence of Languages which
are not Context-Free.

6. The computability and computational complexity

We briefly explain the existence of non-computable problems even if they are formally defined. As an example of such problems, we introduce the Halting Problem. We also briefly explain the introduction of computational complexity and show some topics such as P and NP classes of computational complexity, NP complete class and some examples of NP complete problems.

Class schedule for each professor is as follows.

Kazuyoshi Mori
1,2.
Mathematical Introduction. Itroduction to Formal Languages with Operations of Words and Formal Languages.
3,4,5.
Finite Automata (FA) (Deterministic (DFA), Nondeterministic (NFA), and Nondeterministic with ε-moves (ε-NFA)). Regular Languages(RL).
Relationship between DFA, NFA, and ε-NFA.  Non-Regular Languages (Pumping Lemma). Minimization of Deterministic Finite Automata.
6,7.
Pushdown Automata (PDA)(Deterministic (DPDA) and Nondeterministic (NPDA)).  Context-Free Languages (CFL). Non-Context-Free Languages (Pumping Lemma for CFL).
8.
Midterm Examination.
9,10.
Formal Languages.  Regular Grammars (RG). Linear Grammers (LG). Regular Expressions (RE). Relationship between FA, RG, RE, and RL.
11,12,13.
Context-Free Grammars (CFG).  Derivation tree.  Relationship between NPDA, CFG, and CFL.  Chomsky Normal Forms (CNF). Griebach Normal Forms(GNF).
14.
Other Computing Models, Grammars, and Languages. Hierarchy of Language Classes. Computability and Computational Complexity.

Mohamed Hamada
1. Mathematical background I: Sets, Relations, Functions.
2. Mathematical background II: Graphs, Trees, Proof techniques.
3. Finite Automata: Deterministic finite automata (DFA) with examples
4. Regular expressions, Nondeterministic finite automata (NFA) and NFA to DFA conversion
5. Nondeterministic finite automata with empty move (λ-NFA) and conversion to NFA
6. Deterministic finite automata to regular expression conversion
7. Finite automata minimization
8. Midterm exam
9. Grammar: Regular grammar (RG) and Context-free grammar (CFG)
10. CFG Parsing, and grammar ambiguity
11. Normal forms: Chomsky Normal form (CNF), CFG to CNF conversion, Griebach Normal form (GNF)
12. Regular languages, Non-regular languages and Pumping lemma
13. Context-free languages and Push down automata (PDA)
14. Introduction to Turing machines and computability theory

Taro Suzuki
1. Introduction to automata, grammars and language theory. Mathematical introduction
2-4. finiteautomata: Deterministic finite automata (DFA), Nondeterministic finite automata (NFA), Nondeterministic finite automata with empty moves (λ-NFA)
5. Equivalence of DFA, NFA and λ-NFA
6. DFA Minimization
7. Pushdown automata (PDA)
8. Grammars and languages
9. Midterm exam
10. Classification of grammars (Regular grammars (RG), Context-free  grammars (CFG), Regular languages (RL), Context-free languages (CFL))
11. Normal forms of Context-free grammars (Chomsky Normal form (CNF),  Griebach Normal forms (GNF))
12. Correspondences between automata and grammars
13. Hierarchy of Language Classes (Languages which not Regular and Pumping Lemma for RL, Languages which not Context-free and Pumping for CFL).
14. Computability and Computational Complexity

The correspondence between classes and topics described above may be changed according to the progress of the course.
教科書
/Textbook(s)
No textbook is used. Materials will be distributed from each professor.
成績評価の方法・基準
/Grading method/criteria
The weights of the final exam is 40%. The other grading methods and criteria depend on professors.

Kazuyoshi Mori.
Midterm exam: 30%  Exercise: 30%

Mohamed Hamada
the class activities: 14% (1% per class)  Midterm exam: 20%  Exercise: 26%

Taro Suzuki
Midterm exam: 30%  Exercise: 30%
履修上の留意点
/Note for course registration
Students will be expected to have taken F3 Discrete Systems course. Moreover, they are expected to have fundamental knowledge about not only Programming and Algorithms but also Computer hardware and its behavior.

Language Processing Systems requires the knowledge studied in this course. Especially, in the first part (lexical analysis and syntax analysis) of a language processing system such as a compiler, the knowledge of automata and grammars is indispensable. Therefore, students who will study Language Processing Systems are strongly recommended to enroll this course.
参考(授業ホームページ、図書など)
/Reference (course
website, literature, etc.)
J. Hopcroft, J. Ullman: Introduction to Automata Theory,
Languages and Computation, Addison-Wesley, 1979.

J. L. Hein: Theory of Computation, An Introduction,
Jones and Bartlett, 1996.

M. Sipser: Introduction to the Theory of Computation,
PWS Publishing Co., 1996.

N. Pippenger: Theories of Computability,
Cambridge Univ. Press, 1997.

R. Greenlaw, H. J. Hoover: Fundamentals of the Theory of
Computation, Morgan Kaufmann Pubs.Inc., 1998

A. Meduna: Automata and Languages, Theory and Applications,
Springer, 1999.

J. Hopcroft, R. Motwani, J. Ullman: Introduction to Automata Theory,
Languages, and Computation(3rd ed.), Addison-Wesley, 2006.

P. Linz: An Introduction to Formal Languages and
Automata(5 ed.), Jones and Bartlett, 2012.

The course instructors have work experiences:
Prof. Suzuki was engaged in the development of language processing systems on Prolog inference machines from 1987 to 1990 at MTC Co.,Ltd. Based on his experience, he can teach the basics of language theory and its relationship with language processing systems.
Prof. Mori was engaged in the implementation of REDUCE, a computer algebra system, on SELCOS, a computer system for industrial control at Shinko Electric Co.,Ltd in 1988, where REDUCE runs on RLISP.  Based on his experience he can teach the basics of a theoretical aspect as well as mechanical aspect of computing.


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開講学期
/Semester
2019年度/Academic Year  1学期 /First Quarter
対象学年
/Course for;
3rd year
単位数
/Credits
3.0
責任者
/Coordinator
Nobuyoshi Asai
担当教員名
/Instructor
Nobuyoshi Asai, Yuichi Yaguchi, Konstantin Markov, Taro Suzuki, Yong Liu, Yen Neil Yuwen
推奨トラック
/Recommended track
履修規程上の先修条件
/Prerequisites

更新日/Last updated on 2019/02/18
授業の概要
/Course outline
The study of algorithms is at the very heart of computer science. This course is intended to teach the advanced computer algorithms and techniques for their design and analysis which are not covered in Algorithms and Data Structures 1. After the course the students will have a solid background for this type of activity, as well as for representing algorithms in the format of computer programs.
授業の目的と到達目標
/Objectives and attainment
goals
This course will cover (but not limited to) the following contents: algorithms and their complexity, graph algorithms, heaps, B-trees, matrix multiplication, algebraic path problem, special mathematical algorithms, divide-and-conquer, dynamic programming, recursion, greedy, and algorithm design techniques.
授業スケジュール
/Class schedule
Lecture 01 - Algorithms and their Complexity;
Lecture 02 - Priority Queue and Heap;
Lecture 03 - Graphs and Representations;
Lecture 04 - Weighted Graphs;
Lecture 05 - Shortest Path Problem;
Lecture 06 - Transitive Closure;
            07 - Midterm Exam.
Lecture 08 - Algorithm Design Techniques: Greedy Algorithms;
Lecture 09 - Algorithm Design Techniques: Divide-and-Conquer;
Lecture 10 - Algorithm Design Techniques: Dynamic Programming;
Lecture 11 - Algorithm Design Techniques: Backtracking;
Lecture 12 - Random Number Generators;
Lecture 13 - Randomized Algorithms;
Lecture 14 - Models of Computations.
教科書
/Textbook(s)
T. H. Cormen, C. E. Leiserson, R. L. Rivest, C. Stein, Introduction to Algorithms 3rd Ed. (MIT Press, ISBN-10: 0262033844, ISBN-13: 978-0262033848, soft cover: ISBN-10: 0262533057, ISBN-13: 978-0262533058)
or its Japanese translation:
T. H. Carmen, C. E. Leiserson, R. L. Rivest, C. Stein, 浅野 哲夫 (訳),‎ 岩野 和生 (訳),‎ 梅尾 博司 (訳),‎ 山下 雅史 (訳),‎ 和田 幸一 (訳)アルゴリズムイントロダクション 第3版(世界標準MIT教科書)近代科学社,  第1巻: 基礎・ソート・データ構造・数学(ISBN-10: 4764904063, ISBN-13: 978-4764904064), 第2巻: 高度な設計と解析手法・高度なデータ構造・グラフアルゴリズム(ISBN-10: 4764904071, ISBN-13: 978-4764904071), または総合版 (ISBN-10: 476490408X, ISBN-13: 978-4764904088)
成績評価の方法・基準
/Grading method/criteria
Lab. Exercises: 40%;
Mid-Term Exam: 30%;
Final Exam: 30%;
履修上の留意点
/Note for course registration
The knowledge and skill of the following classes are required:
Linear Algebras 1, 2,
Discrete Systems,
Programming C,
and
Algorithms and Data Structures 1
参考(授業ホームページ、図書など)
/Reference (course
website, literature, etc.)
1. Alfred V. Aho, John E. Hopcroft, Jeffrey D. Ullman. The Design and Analysis of Computer Algorithms (Addison Wesley Professional, 1974, ISBN:0-201-00029-6);
2. Alfred V. Aho, John E. Hopcroft, Jeffrey D. Ullman(著), 野崎昭弘, 野下浩平(訳)『アルゴリズムの設計と解析I』 (サイエンス社, 1977, ISBN:4-7819-0279-0);
3. Alfred V. Aho, John E. Hopcroft, Jeffrey D. Ullman(著), 野崎昭弘, 野下浩平(訳)『アルゴリズムの設計と解析II』 (サイエンス社, 1977, ISBN:4-7819-0280-4);
4. Robert Sedgewick(著), 野下浩平, 星守, 佐藤創, 田口東(訳)『アルゴリズムC 第1巻 基礎・整列』 (近代科学社, ISBN:4-7649-0255-9);
5. Robert Sedgewick(著), 野下浩平, 星守, 佐藤創, 田口東(訳)『アルゴリズムC 第2巻 探索・文字列・計算幾何』 (近代科学社, ISBN:4-7649-0256-7);
6. Robert Sedgewick(著), 野下浩平, 星守, 佐藤創, 田口東(訳)『アルゴリズムC 第3巻 グラフ・数理・トピックス』 (近代科学社, ISBN:4-7649-0257-5);


Reference (coursewebsite, literature, etc.)
Course Website: http://hare.u-aizu.ac.jp/classaa/2019  (N. Asai class)  

(Moodle):http://hi-srv2.u-aizu.ac.jp/ (K. Markov class)


Prof. Asai has practical experience at Wave Front Co. Ltd for numerical simulations, modelings and high performance computings from 1997 to 2000. After joined to U. of Aizu, he has continued with companies on the mentioned topics. These experience is deeply related to this class topics of cost of computation, data structure, and algorithms.

Prof. Suzuki was engaged in the development of C and Lisp language
processing systems implemented on inference machines based on Prolog,
developed in the Fifth Generation Computer Systems national project
from 1987 to 1990 at Mitsubishi Electric Eastern Computer System Co.
Ltd. After joined to U. of Aizu, he has been engaged in the preparation
for the PC Koshien Programming  Division, a programming contest for
high-school students, since 2012.

Prof. Markov has a practical experience at the Advanced Telecommunications Research (ATR) Institute where he worked from 1999 to 2009. This experience includes complex algorithms for search, dynamic programming and optimization which are directly related to the topics of this class.


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開講学期
/Semester
2019年度/Academic Year  1学期 /First Quarter
対象学年
/Course for;
4th year
単位数
/Credits
3.0
責任者
/Coordinator
Mohamed Hamada
担当教員名
/Instructor
Mohamed Hamada
推奨トラック
/Recommended track
履修規程上の先修条件
/Prerequisites

更新日/Last updated on 2019/02/05
授業の概要
/Course outline
Languages processing is a fundamental and vital subject in computer science. It is a subject which has been studied intensively since the early 1950’s and continues to be an important research field today. Languages processing is an important part of the undergraduate curriculum for many reasons:
1. It provides students with a better understanding of and appreciation for       programming languages.
2. The techniques used in languages processing can be used in other applications with command languages.
3. It provides motivation for the study of theoretic topics.
授業の目的と到達目標
/Objectives and attainment
goals
Students understand the role of languages processing systems such as compilers and translators, the processing methods of languages, the importance of compilers, and the relation between theory and practice (that is, formal language theory and languages processing systems).
授業スケジュール
/Class schedule
1. Introduction to compiler and interpreter
2. Anatomy of the compiler and compiler structure
3. Lexical Analysis, regular expression, finite automata and LEX tools
4. Syntax analysis and introduction to parsing
5. Top-down parsing techniques: predictive parser
6. Bottom-up parsing techniques: shifet-reduce parser
7. Automatic compiler generator tools: LEX and YACC
8. Midterm exam
9. Semantics analysis: Abstract syntax tree and scope
10. Construction of symbol table
11. Type checker and type system
12. Intermediate representations and intermediate code generation
13. Abstract machine and code generation
14. Final review
教科書
/Textbook(s)
Several book recommendations and other reading materials will be described in lectures
成績評価の方法・基準
/Grading method/criteria
Class activities: 14 %
Exercise: 26 %
Midterm exam: 20 %
Final Exam: 40 %
履修上の留意点
/Note for course registration
Automata theory
参考(授業ホームページ、図書など)
/Reference (course
website, literature, etc.)
Will be given in lecture


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開講学期
/Semester
2019年度/Academic Year  4学期 /Fourth Quarter
対象学年
/Course for;
3rd year
単位数
/Credits
3.0
責任者
/Coordinator
Yohei Nishidate
担当教員名
/Instructor
Yohei Nishidate, Kohei Kitazato, Yong Liu
推奨トラック
/Recommended track
履修規程上の先修条件
/Prerequisites

更新日/Last updated on 2019/02/05
授業の概要
/Course outline
This course provides basics of numerical methods. Applications of numerical methods in all main areas of floating-point computing are covered. The course focuses on ideas and techniques that are widely used in Computational Science.
授業の目的と到達目標
/Objectives and attainment
goals
Students who successfully complete this course will be able to: demonstrate an understanding of the main numerical methods; develop computer programs based on numerical methods; use methods of numerical analysis in practical problems.
授業スケジュール
/Class schedule
Week 1: Introduction. Computer Precision
- Introduction to numerical analysis
- Floating-point representation
- Determination of computer parameters

Week 2: Errors of Floating-point Computations
- Loss of significance
- Error propagation
- Function evaluation

Week3: Zero of a Function
- Bisection method
- Newton's method
- Newton's method for nonlinear system of equations

Week 4: Linear Algebraic Equations
- Vectors and matrices
- Gauss elimination
- LU decomposition

Week 5: Matrix Inversion. Matrix Eigenvalues
- Determinant of a matrix
- Matrix inverse
- Eigenvalues of a matrix. Jacobi diagonalization

Week 6: Interpolation and Curve Fitting
- Lagrange polynomials
- Newton polynomials
- Least-squares curve fitting

Week 7: Spline Interpolations
- Piecewise cubic spline
- B-spline

Week 8:  Mid-term Examination

Week 9: Numerical Differentiation and Integration
- Forward difference and central difference
- Numerical integration. Trapezoidal rule
- Simpson’s rules
- Gauss quadrature

Week 10: Ordinary Differential Equations
- Euler’s method
- Predictor-corrector method
- Runge-Kutta methods
- First-order systems

Week 11: Partial Differential Equations
- Finite difference method
- Laplace equation
- Poisson equation
- Derivative boundary conditions

Week 12: Methods for Sparse Matrices
- Matrix storage
- Direct solution methods
- Iterative methods

Week 13: Random Number Generation and Monte Carlo Method
- Linear Congruential Generators
- Integration by Monte Carlo Method

Week 14: Review
教科書
/Textbook(s)
Lecture notes.

成績評価の方法・基準
/Grading method/criteria
Exercises 50%.
Midterm examination 20%
Final examination 30%.
履修上の留意点
/Note for course registration
Prerequisites: C Programming, Algorithms and Data Structures.
Related courses: Calculus, Linear Algebra, Java Programming.

Formal prerequisites:F1 Algo.and Data Struct.
参考(授業ホームページ、図書など)
/Reference (course
website, literature, etc.)
数値で学ぶ計算と解析,金谷健一,共立出版.


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開講学期
/Semester
2019年度/Academic Year  1学期 /First Quarter
対象学年
/Course for;
3rd year
単位数
/Credits
3.0
責任者
/Coordinator
Rentaro Yoshioka
担当教員名
/Instructor
Rentaro Yoshioka, Alexander P. Vazhenin, Incheon Paik, Vitaly V. Klyuev, Yan Pei, Julian Villegas
推奨トラック
/Recommended track
履修規程上の先修条件
/Prerequisites

更新日/Last updated on 2019/03/04
授業の概要
/Course outline
Software Engineering is a field concerning technologies and methods related to software development. It is about how software should be built, how they should be managed and includes a wide range of both theoretical and practical knowledge. As the presence of software in our daily life seems only to increase, the importance of software engineering is also increasing. It is also one of the fundamental knowledge of software that any engineer in the IT field should understand.
In this course, we first study the processes involved in the making of software and understand how they affect the final product. For each stage of the process, we consider issues that need to be solved and visit related technologies and methods that are available. In presentation of available technologies and methods, the goal is to understand the issues and their representative solutions. Both historical and cutting-edge technologies will be introduced as necessary. We will not cover the differences that arise with different forms of software (such as, embedded, web-based, parallel, etc.) but focus on more general and common aspects.
In summary, this course focuses on understanding the knowledge and technology sets comprising Software Engineering.
In exercises, to help the understanding of basic knowledge, students will work on concrete exercise problems. Each exercise class covers one stage of the development process, and the work required in each stage will be covered one-by-one. Each exercise is designed with a real-world application in mind it is possible to realize issues that are difficult to notice only by theory and basics. After completing all the exercises, students will experience a typical development process and understand its role and effects.
授業の目的と到達目標
/Objectives and attainment
goals
1. Be able to explain the knowledge and technologies involved in Software Engineering
2. Be able to explain the typical stages of a development process and explain their characteristics and issues involved.
3. Be able to develop an application by employing a typical process and model
授業スケジュール
/Class schedule
Day 1
Lecture : Introduction to Software Engineering
Exercise : Orientation and Requirements Definition 1
Day 2
Lecture: Processes & Requirements Definition
Exercise : Requirements Definition 2
Day 3
Lecture : Requirements Definition 2
Exercise : Requirements Phase - Feedback & Self-check
Day 4
Lecture : Requirements Definition 3
Exercise : Analysis 1
Day 5
Lecture : Analysis – Architectural Design
Exercise : Analysis 2
Day 6
Lecture : Analysis – Architectural Design 2
Exercise : Analysis 3
Day 7
Lecture : Analysis – Architectural Design 3
Exercise : Analysis – Feedback & Self-check
Day 8
Lecture : Design – Module Design
Exercise : Detailed Design 1
Day 9
Lecture : Design – Modules Design 2
Exercise : Detailed Design 2
Day 10
Lecture : Design – Module Design 3
Exercise : Detailed Design 3
Day 11
Lecture : Programming
Exercise : Detailed Design Phase – Feedback & Self-check
Day 12
Lecture : Test
Exercise : Development & Test
Day 13
Lecture : Unit Test, Integration Test, and System Test
Exercise : Development & Test
Day 14
Lecture : Summary & Future Perspective
Exercise : Development & Test Phase – Feedback & Self-check
教科書
/Textbook(s)
Handouts for each lecture and exercises will be downloadable from course web-sight.
Related reading material will be instructed during lectures.
成績評価の方法・基準
/Grading method/criteria
1. Quiz 5%
2. Final Examination 60%
3. Exercise 35%
*Exercises are evaluated by the level at which they satisfy the requirements of the task.
* Exercises are expected to be submitted during class. The final due date of each exercise is at 24:00 of the day before the next lecture.
*Final exam will include problems to check understanding of the exercise topics as well.
履修上の留意点
/Note for course registration
・All exercises should be performed individually (Is not team work).
・Discussions with fellow students is recommended but copying of work of others is strictly prohibited and will be penalized.
・Students are requested, if necessary, to self-study necessary knowledge and skills (including, details of UML, Java, Astah) outside of course hours.
参考(授業ホームページ、図書など)
/Reference (course
website, literature, etc.)
http://borealis.u-aizu.ac.jp/classes/se1
Moodle http://sealpv0.u-aizu.ac.jp:20000/

The course instructor has working experiences:A currently active software engineer (with more than 30 years’ experience) and faculty with previous software development experience (including practical software development in industry) will jointly provide lectures and exercises.


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開講学期
/Semester
2019年度/Academic Year  2学期 /Second Quarter
対象学年
/Course for;
3rd year
単位数
/Credits
3.0
責任者
/Coordinator
Evgeny Pyshkin
担当教員名
/Instructor
Evgeny Pyshkin, Maxim Mozgovoy, Julian Villegas, Wanming Chu, Yen Neil Yuwen, Kenta Ofuji
推奨トラック
/Recommended track
履修規程上の先修条件
/Prerequisites

更新日/Last updated on 2019/02/28
授業の概要
/Course outline
The course is focused on representing the following conceptual units (please see the class schedule for implementation details):
- Foundation Concepts necessary for introducing basic data management principles,
- Data modeling approaches, implementation and practical use.
- Methods and organization of data storage, versioning, distribution and backup.
- Accessing and storing data with using database systems and specialized data management systems.
授業の目的と到達目標
/Objectives and attainment
goals
The objective of this course is to introduce data management as a research and technology domain with its distinct agenda, to explain data models and modeling approaches used in present-day information systems. A particular emphasis will be made on real-life daily scenarios of data modeling, storage, and retrieval methods. The course will serve as a basis for subsequent specialized courses such as Database management systems, or Operating systems.

After course completion the students will have the following learning outcomes:
- Understanding concepts of information and data.
- Knowledge about genesis of information systems and ability to create entity-relationship data models.
- Understanding data representation levels and ability to develop conceptual and physical data models.
- Ability to determine which information management methods and/or techniques are appropriate for a given problem or within the given subject domain.
- Understanding data lifecycle phases and necessary techniques relevant to a particular phase.
- Understanding a concept of metadata, knowledge about existing metadata standards and ability to use appropriate metadata formats.
- Understanding a concept of persistent data structure, knowledge about existing approaches for managing stored data and ability to deploy and use version control systems.
- Understanding approaches, models and conventions for data sharing, storage and reuse.
- Understanding object-oriented models of data and processes ad ability to use modeling tools (UML).
- Understanding the models and concepts used to build structured data storage systems, such as databases.
授業スケジュール
/Class schedule
Class 1. Introducing the course. Foundation Concepts.
From Information to Data. Understanding information, data, knowledge.
Information systems. Information systems as socio-technical systems. Human-centric development as a response to societal problem. Understanding data science and engineering.
Information capture and representation. Abstract data type. Data retrieval. Clustering and classification. Basic information storage and retrieval concepts. Understanding acquisition, representation, digitalization, processing and transformation.

Class 2.  Intro to Data Modeling: Entity-Relationship Model.
Understanding data modeling. Why do we create models?
Major concepts. Entity Relationship model (ER model): purpose, limitation, major elements. Mapping natural language.
ER model in use. Creating an ER model. From subject domain to information system vision.Example of ER modeling: course registration system. More examples of ER modeling.

Class 3. Object-Oriented Modeling (part 1).
Data modeling and abstraction, data and information storage: concepts, mechanisms, implementations.
Data modeling foundations. Conceptual models (entity-relationship) revisited. Relational data models. Object-oriented analysis and design. Intro to UML.

Class 4. Unified Models.
Object-Oriented Modeling and Unified Models.
Modeling languages and tools (e.g. UML). Semi-structured data models. Introduction to XML.
UML modeling scopes. UML Diagrams.

Class 5. Persistent Structures, Versioning and Continuous Integration.
Persistence and versioning. Persistence types: partial, full, confluent, functional.
Version control systems for software developers. Motivation and three generations of VCSs. Typical operations and workflow. The problem of binary data versioning.

Class 6. Version Control Systems in Practice.
Versioning and team project development via version control systems. Centralized and distributed version control systems. Basic operations and typical workflow.

Class 7. Introduction to Database Systems (part 1).
Introduction into relational database management system. Designing databases with Major approaches to building databases. Evolution of database systems. Data modeling and database design. Designing databases with entity-relationship models.

Class 8. Introduction to Database Systems (part 12.
Query languages. Storage and indexing. Query processing. Transaction processing.
Structured query language. Types of queries. Adding, changing, and retrieving data stored in a database.

Class 9. Spreadsheet Systems (part 1).
Spreadsheets as a data organization and management instrument.  Basic functions of spreadsheet software. Typical use cases.

Class 10. Spreadsheet Systems (part 2).
Advanced functions of spreadsheet systems. Charts, add-ins and programming.

Class 11. Specialized Data Management Systems and Workplace Organization.
Diversity of data management scenarios. Task and issue management systems. Mind mapping software. Note-taking tools.
Organizing your reading and references. Software packages and file management systems.

Class 12. Data Redundancy and Backup.
Data Process. Data lifecycle phases. Consider your goals. What data are you collecting? How do you plan to keep this data? What do you need to be able to use it and share it later? Why and how to backup your data? Types of backups: incremental, differential, mirror. Redundant data storage methods. RAID arrays. Reliability of data media (flash memory, HDD, CD/DVD, tapes).

Class 13. Data Models and Software.
Human-centric development as a response to societal problem. Understanding data science and engineering.
Quality issues: reliability, scalability, efficiency, effectiveness.

Class 14. Optional topics. Q&A session. Course Review and Course Summary.
教科書
/Textbook(s)
Information on textbooks and helpful resources is provided on the course web page.
成績評価の方法・基準
/Grading method/criteria
The final grade is calculated based on the following weights:
- Tests and quizzes during lecture and hands-on time – 40%
- Exercises and individual projects – 30%
- Final test – 30%

Additional bonus points may be given by the course instructors for active participation in classroom activities and high quality creative exercise solutions and be used to compensate the possible grade deficit for major course activities up to 20%.
履修上の留意点
/Note for course registration
This course is based on Programming.Intro and C.Programming delivering primary knowledge of programmable computational process and data type concepts. We also expect that the students have basic understanding of algebraic models studied in the courses of discrete mathematics.
参考(授業ホームページ、図書など)
/Reference (course
website, literature, etc.)
The course lecturers have extensive working experiences in software design, development and testing. Prof. Evgeny Pyshkin worked as a software engineer for St. Petersburg Polytechnic University and participated in software projects with Spectrasize (Switzerland) as a developer and a project manager. Prof. Maxim Mozogovoy worked in joint software projects with industry partners as software developer, software QA engineer and project team leader. Prof Ofuji has a 10-year work experience in a private company and a research institute, involved in a real-world, energy-related statistical data analysis.

Official Course Web Page: http://web-int.u-aizu.ac.jp/~pyshe/courses/idm/.
The access to the course on Moodle: https://aizudc.gnomio.com.


Responsibility for the wording of this article lies with Student Affairs Division (Academic Affairs Section).

E-mail Address: sad-aas@u-aizu.ac.jp