2024年度 シラバス大学院

CS教育研究領域　(コンピュータサイエンス)

2024/05/03  現在

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開講学期
／Semester		 2024年度／Academic Year 　1学期 ／First Quarter
対象学年
／Course for;		 1年 , 2年
単位数
／Credits		 2.0
責任者
／Coordinator		 劉　勇
担当教員名
／Instructor		 劉　勇
推奨トラック
／Recommended track		 －
先修科目
／Essential courses		 PL02 C Programming
PL03 JAVA Programming I
PL04 C++ Programming
FU01 Algorithms and Data Structures I
更新日／Last updated on 2024/01/24
授業の概要
／Course outline		 An intelligent system must have at least the following means (手段):

1)  A means to access and acquire information.

2)  A means to integrate, abstract, and be aware of the information.

3)  A means to change and to adapt to the environment based on acquired information.

The goal of neural network research is to realize an intelligent system using the human brain as a single model to realize all of the above means. There are many research topics in this area, for example

1)  How to use neural networks to represent/acquire information/knowledge?

2)  How to use neural networks to integrate, abstract, and be aware of the information?

3)  How to change a neural network to adapt to the environment?

This course introduces the basic models, learning algorithms, and some applications of neural networks. After this course, we should be able to know how to use neural networks for solving some practical problems such as pattern recognition, pattern classification, function approximation, data visualization, and so on.
授業の目的と到達目標
／Objectives and attainment
goals		 In this course, we will study the following topics:

1)  Basic neuron models: McCulloch-Pitts model, nearest neighbor model, radial basis function model, etc.

2)  Basic neural network models: multilayer neural network, self-organizing neural network, associative memory, radial basis function neural network, support vector machine, neural network tree, etc.

3)  Basic learning algorithms: delta learning rule, back propagation, winner take all, self-organizing feature map, learning vector quantization, etc.

4)  Applications: character recognition, function approximation, data visualization, etc.
授業スケジュール
／Class schedule		 1)  Introduction: A brief introduction of this course.

2)  Fundamental concepts: Neuron models and the general learning rule.

3)  Multilayer neural networks: Structure and the back propagation learning algorithm for multilayer perceptron (MLP).

4)  Team project - I: Learning of MLP for solving simple problems.

5)  Associative memory: Hopfield neural network, energy function, and convergence.

6)  Team project II: Application of Hopfield neural network to image restoration.

7)  Self-organizing neural networks: Kohonen neural network, pattern clustering, and the winner-take-all learning algorithm.

8)  Team project III: Pattern classification using self-organizing neural networks.

9)  Self-organizing feature map: Dimensionality reduction and data visualization based on the self-organizing feature map algorithm.

10)  Team project IV: Visualization of high dimensional patterns.

11)  RBF neural networks: Radial basis neural network and support vector machines.

12)  Team project V: Pattern recognition based on SVM.

13)  Neural network trees: Hybridization of neural networks and the decision tree.

14)  Presentation of projects.
教科書
／Textbook(s)		 No textbook. Teaching materials will be available on the course page .
成績評価の方法・基準
／Grading method/criteria		 Projects: 75 (15 x 5)
Final presentation: 25
履修上の留意点
／Note for course registration		 Students are required to do 5 team projects for implementing the taught neural network models by using C, or C++, or Java languages.
参考(授業ホームページ、図書など)
／Reference (course
website, literature, etc.)		 1) Ian Goodfellow and Yoshua Bengio and Aaron Courville, Deep Learning, MIT Press, 2016.

2)  Jacek M. Zurada, Introduction to Artificial Neural Systems, PWS Publishing Company, 1995.

3)  Simon Haykin, Neural Networks: A Comprehensive Foundation, Macmillan College Publishing Company, 1994.

4)  Mohamad H. Hassoun, Foundamentals of Artificial Neural Networks,The MIT Press, 1995.

5)  Laurene Fausett, Fundamentals of Neural Networks: Architectures, Algorithms, and Applications, Prentice Hall International, Inc., 1994.

6)  B. D. Ripley, Pattern Recognition and Neural Networks, Cambridge University Press., 1996.

7)  URL of this course: http://web-ext.u-aizu.ac.jp/~qf-zhao/TEACHING/NN-I/nn1.html


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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;		 1年 , 2年
単位数
／Credits		 2.0
責任者
／Coordinator		 劉　勇
担当教員名
／Instructor		 劉　勇
推奨トラック
／Recommended track		 －
先修科目
／Essential courses		 CSA01 Neural Networks  I: Fundamental Theory and Applications
更新日／Last updated on 2024/01/24
授業の概要
／Course outline		 The course starts from an overview of evolutionary computation, and a simple example of evolutionary optimization so that the students could quickly grasp the basic ideas of nature-inspired techniques. In the following lectures, design examples in game, learning systems, and intelligent systems will be given. The aim of this course is to let students learn nature-inspired design by examples.
授業の目的と到達目標
／Objectives and attainment
goals		 1. To know what nature-inspired design techniques are and how they are applied to some real problems.

2. To understand the advantages and disadvantages of nature-inspired design techniques compared to other traditional design techniques.

3. To investigate potential applications of nature-inspired techniques to some real world problems.
授業スケジュール
／Class schedule		 Lecture 01:   An Overview of Evolutionary Computation
Lecture 02:   Evolutionary Programming
Lecture 03 :  Fast Evolutionary Programming
Lecture 04 :  Evolutionary Game
Lecture 05 :  Search Operators and Representation
Lecture 06:   Project 1 on Evolutionary Optimization
Lecture 07 :  Selection and Recombination
Lecture 08 :  Evolutionary Robots
Lecture 09 :  Introduction to Neural Networks: Part 1
Lecture 10 :  Introduction to Neural Networks: Part 2
Lecture 11 :  Project 2 on Evolutionary Learning
Lecture 12 :  Evolutionary Artificial Neural Networks
Lecture 13 :  Evolutionary Neural Network Ensembles
Lecture 14 :  Final Presentations and Discussions
教科書
／Textbook(s)		 No textbook. Lecture notes will be available on the course page.
成績評価の方法・基準
／Grading method/criteria		 The students will be asked to design and simulate an evolutionary system, and give presentations on their projects.

Projects: 60 (30x2, two projects)
Final presentation: 40
履修上の留意点
／Note for course registration		 This course will also introduce genetic algorithms after the graduate school course on genetic algorithms was removed .
参考(授業ホームページ、図書など)
／Reference (course
website, literature, etc.)		 1. Some reference papers will be shown in lecture notes.

2. Ian Goodfellow and Yoshua Bengio and Aaron Courville, Deep Learning, MIT Press, 2016.


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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;		 1年 , 2年
単位数
／Credits		 2.0
責任者
／Coordinator		 森　和好
担当教員名
／Instructor		 森　和好
推奨トラック
／Recommended track		 －
先修科目
／Essential courses		 －
更新日／Last updated on 2024/01/24
授業の概要
／Course outline		 本講義のシラバスは英語版を参照してください。
授業の目的と到達目標
／Objectives and attainment
goals		 This course is concerned with the multidimensional systems
theory. This theory includes multidimensional control system and image
processing, and so on. We will proceed precisely with mathematical
descriptions.
授業スケジュール
／Class schedule		 1. Scalar 2-D Input/Output Systems
2. Stability
3. Structural Stability
4. Multi-Input/Multi-Output Systems
5. Stabilization of Scalar Feedback Systems
6. Characterization of Stabilizers for Scalar Systems
7. Stabilization of Strictly Causal Transfer Matrices
8. Characterization of Stabilizers for MIMO Systems
9. Stabilization of Weakly Causal Systems
10. Stabilization of MIMO Weakly Causal Systems
教科書
／Textbook(s)		 To be distributed.
成績評価の方法・基準
／Grading method/criteria		 Final examination and/or Reports
履修上の留意点
／Note for course registration		 The language is English.
If all students can understand Japanese, Japanese may be used in addtion.
参考(授業ホームページ、図書など)
／Reference (course
website, literature, etc.)		 1. Multidimensional Systems Theory (2nd Ed). D.Reidel Publishing, 2003. (Reference)
2. Schaum's Outline of Theory and Problems of Signals and Systems 3rd Ed.(Schaum's
Outlines) (Reference)


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開講学期
／Semester		 2024年度／Academic Year 　4学期 ／Fourth Quarter
対象学年
／Course for;		 1年 , 2年
単位数
／Credits		 2.0
責任者
／Coordinator		 ハマダ　モハメド
担当教員名
／Instructor		 ハマダ　モハメド
推奨トラック
／Recommended track		 －
先修科目
／Essential courses		 Automata and Langauges
更新日／Last updated on 2024/01/19
授業の概要
／Course outline
The models of computations will be introduced and the term rewriting
systems (TRS), as a universal model of computation, and its major
properties such as termination and confluence will be discussed. Term
rewriting is a branch of theoretical computer science which combines
elements of logic, universal algebra, automated theorem proving and
functional programming. Its foundation is equational logic. TRS
constitutes a Turing- complete computational model which is very close
to functional programming. It has applications in Algebra, recursion
theory, software engineering and programming languages. In general
TRSs apply in any context where efficient methods for reasoning with
equations are required.
授業の目的と到達目標
／Objectives and attainment
goals		 This course gives students the fundamental concepts of the
computational models and the concept of rewriting systems and its
applications in many areas of theoretical computer science. It also
give the students more understanding of the major properties of term
rewriting systems.
授業スケジュール
／Class schedule		 1. Introduction to models of computation
2. Finite automata as a model of computation for formal languages
3. Turing machines as a powerful model of computation
4. Rewriting systems as a general-purpose model of computation
5. Term rewriting systems
6. Unification, most general unifier and unification algorithm
7. Midterm Report/Exam/presentation
8. Church-Rosser property, Confluence, and Local confluence
9. Critical pairs (CP) and CP algorithm
10. Termination of rewriting systems
11. Completion and Knuth-Bendix algorithm
12. Other rewriting systems
13. General review
14. Final report presentation/Exam
教科書
／Textbook(s)		 1. F. Baader and T. Nipkow, Term Rewriting and All That, Cambridge
University Press, 1998.
2. Other materials related to the topics will be introduced in the
class. Various materials will be prepared
成績評価の方法・基準
／Grading method/criteria		 1. Class activities: 14%
2. Exercise: 26%
3. Midterm exam/report: 20%
4. Final exam/report: 40%
履修上の留意点
／Note for course registration		 As this course is given to students who have not studied the
fundamentals of term rewriting systems, there is no prerequisites. But
we expect students have some basic courses such as discrete
mathematics and/or algebra.
参考(授業ホームページ、図書など)
／Reference (course
website, literature, etc.)		 Will be given during lectures.


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開講学期
／Semester		 2024年度／Academic Year 　1学期 ／First Quarter
対象学年
／Course for;		 1年 , 2年
単位数
／Credits		 2.0
責任者
／Coordinator		 浅井　信吉
担当教員名
／Instructor		 浅井　信吉
推奨トラック
／Recommended track		 －
先修科目
／Essential courses		 線形代数１

線形代数２

応用代数

数値解析
更新日／Last updated on 2024/01/26
授業の概要
／Course outline		 This is a topics course: several recent topics of numerical
computation will be selected and discussed in detail.
・Elements of the Hilbert space.
・The eigenvalue problem for infinite matrices.
・Application to the special function computation.
・Visualization.
・Introduction to high-performance computing.
Case studies include introduction to important software packages and
the Internet usage.

授業の目的と到達目標
／Objectives and attainment
goals		 Study some application of functional analysis to numerical computation.
授業スケジュール
／Class schedule		 1. Elements of the Hilbert space(1)
2. Elements of the Hilbert space(2)
3. The eigenvalue problem for infinite matrices(1)
4. The eigenvalue problem for infinite matrices(2)
5. The eigenvalue problem for infinite matrices(3)
6. The eigenvalue problem for infinite matrices(4)
7. Application to the special function computation(1)
8. Application to the special function computation(2)
9. Application to the special function computation(3)
10. Application to the special function computation(4)
11. Visualization(1)
12. Visualization(2)
13. Introduction to high-performance computing(1)
14. Introduction to high-performance computing(2)

教科書
／Textbook(s)		 A. E. Taylor, D. C. Lay, Introduction to Functional Analysis, Kriger Pub., 1980
G. F. Simmons, Intruduction to Topology and MOdern Analysis, Mc-Graw Hill, 1963
成績評価の方法・基準
／Grading method/criteria		 Quizzes, activities and/or reports.


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開講学期
／Semester		 2024年度／Academic Year 　3学期 ／Third Quarter
対象学年
／Course for;		 1年 , 2年
単位数
／Credits		 2.0
責任者
／Coordinator		 ハマダ　モハメド
担当教員名
／Instructor		 ハマダ　モハメド
推奨トラック
／Recommended track		 －
先修科目
／Essential courses		 Discrete Mathematics
更新日／Last updated on 2024/01/19
授業の概要
／Course outline		 This course gives students advanced topics in the theory of automata and languages. The characterization of language classes, which is one of the most important themes in the formal language theory, will be introduced. Especially, the homomorphic characterizations of language classes will be discussed in detail. Moreover, some applications of
formal language theory will be discussed.

授業の目的と到達目標
／Objectives and attainment
goals		 Students can be familiar to the automata and languages and recognize the importance of the theory of automata and languages, and have enough knowledge to read and understand advanced papers in this field completely.

授業スケジュール
／Class schedule		 1. Introduction and background
2. Review of the theory of automata and languages
3. Methods to describe infinite sets
4. Grammars as generating systems of infinite sets
5. Automata as recognizing systems of infinite sets
6. Chomsky hierarchy of language classes
7. Relation among grammars and automata
8. Midterm exam or report
9. Topics on the theory of automata and languages
10. Subclasses languages defined by automata with restrictions
11. Subclasses languages defined by grammars with restrictions
12. Operations on languages
13. Homomorphic characterization of language classes
14. Applications of the theory of automata and languages
教科書
／Textbook(s)		 We do not specify textbooks but introduce books related to the topics in the class. Various materials will be prepared.


成績評価の方法・基準
／Grading method/criteria		 1. Class activities: 14%
2. Exercise: 26%
3. Midterm exam/Report: 25%
4. Final exam: 35%
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.)		 There are so many good textbooks in this field. Some of them will be introduced in the class and some are given here for students' convenience.

J. Hopcroft, J. Ullman: Introduction to Automata Theory, Languages and Computation, Addison-Wesley, 1979.

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

P. Linz: An Introduction to Formal Languages and Automata(3 ed.),
Jones and Bartlett, 2001.

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,
Principles and Proctice, Morgan Kaufmann Pub. Inc., 1998.

A. Maruoka, Concise Guide to Computation Theory, Springer, 2011.


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開講学期
／Semester		 2024年度／Academic Year 　1学期 ／First Quarter
対象学年
／Course for;		 1年 , 2年
単位数
／Credits		 2.0
責任者
／Coordinator		 渡部　繁
担当教員名
／Instructor		 渡部　繁
推奨トラック
／Recommended track		 －
先修科目
／Essential courses		 －
更新日／Last updated on 2024/01/11
授業の概要
／Course outline		 学部レベルの数学（フーリエ解析, 複素関数論, 位相空間論）をよく理解している人
を対象にした, より進んだ解析学の授業.
授業の目的と到達目標
／Objectives and attainment
goals		 関数空間論への入門としてのフーリエ解析が理解できる.
ヒルベルト空間論の基礎が理解できる.
直交多項式によるフーリエ展開が理解できる.
授業スケジュール
／Class schedule		 １〜２　 学部で習った数学の復習
３〜６　 関数空間論入門
７〜１０    関数解析入門
１１〜１４  直交多項式によるフーリエ展開
教科書
／Textbook(s)		 特になし
成績評価の方法・基準
／Grading method/criteria		 レポート １００％
履修上の留意点
／Note for course registration		 フーリエ解析, 複素関数論, 位相空間論を履修していること.


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開講学期
／Semester		 2024年度／Academic Year 　3学期 ／Third Quarter
対象学年
／Course for;		 1年 , 2年
単位数
／Credits		 2.0
責任者
／Coordinator		 浅井　和人
担当教員名
／Instructor		 浅井　和人
推奨トラック
／Recommended track		 －
先修科目
／Essential courses		 －
更新日／Last updated on 2024/01/19
授業の概要
／Course outline		 授業実施形態: 対面授業と遠隔授業の併用

本講においては，代数系および組合せ論におけるさまざまな topics の紹介を行う． 本年度は主に代数系に注目し，有限体の話題を取り上げたいと考えている．有限体 F_q は ガロアが初めに考えたとされるため，ガロア体とも呼ばれており，有限 (q) 個の元のなす体である． F_q の構造は元の個数 q によってただひとつに定まり，また q は素数のべきでなければならない．

われわれは多項式環からはじめて，素体，有限多項式体，体の拡大，分解体，有限体の構造， 原始元，フローベニウスサイクル，円分多項式，有限体の間の関数などの話題について学びたい． 一般に数と言えば実数あるいは複素数であることを前提としてしまっているが， ここではより抽象的な『数』および『環』『体』などにふれることで，より抽象的，数学的な思考力を 育成することをひとつの目標にしている．

このように有限体は純数学的な対象であり，数学の研究においては，通常の数における理論を 有限体の場合に拡張するといったことはひんぱんに行われているが，有限体はなにも純粋数学のみで 扱われるわけではない．むしろ実験計画，コーディング理論，論理回路等への応用面で極めて有用であり， 理学者，工学者にとって重要な対象であることを付け加えておく．
授業の目的と到達目標
／Objectives and attainment
goals		 多項式環，素体，正標数，(準)同型写像，体の拡大，分解体，q元体の一意性，有限体の構造，原始元， フローベニウスサイクル，円分多項式．
授業スケジュール
／Class schedule		 1. 多項式環．
2. 素体 F_p．
3. 準同型写像と同型写像．
4. 有限多項式体と体の拡大．
5--6. 有限体．
7--8. 有限体の構造．
9--10. 原始元．
11--12. フローベニウスサイクル．
13--14. 円分多項式．
教科書
／Textbook(s)		 1. ハンドアウト: Algebraic Systems and Combinatorics: -- Finite Fields --, by K. Asai

2. Introduction to Finite Fields and Their Applications, Revised ed. (1994), Cambridge University Press, by R. Lidl, H. Niederreiter

3. Finite Fields (Encyclopedia of Mathematics and its Applications) (1997), Cambridge University Press, by R. Lidl, H. Niederreiter

4. 組合せ理論とその応用 (1979)，岩波全書316，高橋磐郎
成績評価の方法・基準
／Grading method/criteria		 レポート:80％
プレゼンテーション:20%
履修上の留意点
／Note for course registration		 重要な関連科目：応用代数，線形代数 I,II．
参考(授業ホームページ、図書など)
／Reference (course
website, literature, etc.)		 授業用ホームページ: http://web-ext.u-aizu.ac.jp/~k-asai/classes/class-texts.html


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開講学期
／Semester		 2024年度／Academic Year 　3学期 ／Third Quarter
対象学年
／Course for;		 1年 , 2年
単位数
／Credits		 2.0
責任者
／Coordinator		 本間　道雄
担当教員名
／Instructor		 本間　道雄
推奨トラック
／Recommended track		 －
先修科目
／Essential courses		 －
更新日／Last updated on 2024/01/23
授業の概要
／Course outline		 This course deals with several basic problems in natural sciences in order to show how the information theory and various computational methods are utilized in the analysis of practical systems.
授業の目的と到達目標
／Objectives and attainment
goals		 At the end of the course the students should be able to:
(1) explain the importance and advantages of various numerical methods in the analysis of physical systems.
(2) design a suitable model and write a program for solving practical problems.
授業スケジュール
／Class schedule		 (1) Introduction
- Numerical derivative, integral, and root finding
- Exercise 1
(2) Differential equation 1
- Initial value problem
- Exercise 2
(3) Differential equation 2
- Boundary value problem
- Exercise 3
(4) Matrix manipulation 1
- Matrix inversion
- Exercise 4
(5) Matrix manipulation 2
- Eigenvalue problem
- Exercise 5
(6) Monte Carlo method 1
Random numbers and sampling of random variables
- Exercise 6
(7) Monte Carlo method 2
Monte Carlo integrals and simulations
- Exercise 7
教科書
／Textbook(s)		 Lecture materials will be provided on the LMS.
成績評価の方法・基準
／Grading method/criteria		 Reports (100%)
Students should submit a report on the problem given in each exercise class.
履修上の留意点
／Note for course registration		 Prerequisites: Students should have some experience and knowledge of basic physics (classical mechanics, electricity and magnetism, quantum mechanics, statistical mechanics) and programming.
参考(授業ホームページ、図書など)
／Reference (course
website, literature, etc.)		 (1) An introduction to computer simulation methods : applications to physical systems 2nd ed.
Harvey Gould and Jan Tobochnik
Addison-Wesley, c1996
(2) Computational physics : FORTRAN version
Steven E. Koonin and Dawn C. Meredith
Addison-Wesley, c1990


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開講学期
／Semester		 2024年度／Academic Year 　3学期 ／Third Quarter
対象学年
／Course for;		 1年 , 2年
単位数
／Credits		 2.0
責任者
／Coordinator		 藤津　明
担当教員名
／Instructor		 藤津　明
推奨トラック
／Recommended track		 －
先修科目
／Essential courses		 －
更新日／Last updated on 2024/01/11
授業の概要
／Course outline		 This course provides recent developements
in high energy particle physics.
授業の目的と到達目標
／Objectives and attainment
goals		 At the end of the course the students should:

1. have basic knowledge of high energy physics
2. know how to use computer to study theory of high energy physics
授業スケジュール
／Class schedule		 1. Basic concepts of quantum field theory
2. Path integral formulation
3. Lattice field theory
4. Gauge field theory
5. Superstring theory
6. Quantum gravity
教科書
／Textbook(s)		 Hands out will be provided.
成績評価の方法・基準
／Grading method/criteria		 Reports 50%  and Examination 50%.
参考(授業ホームページ、図書など)
／Reference (course
website, literature, etc.)		 Office hour: Monday, Thursday 1,2,3,4 period


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開講学期
／Semester		 2024年度／Academic Year 　3学期 ／Third Quarter
対象学年
／Course for;		 1年 , 2年
単位数
／Credits		 2.0
責任者
／Coordinator		 成瀬　継太郎
担当教員名
／Instructor		 成瀬　継太郎, 土屋　貴裕
推奨トラック
／Recommended track		 －
先修科目
／Essential courses		 －
更新日／Last updated on 2024/01/25
授業の概要
／Course outline		 This is a graduate school course on stochastic processes and applications. We focus on several classes of elementary stochastic processes which are often used in various applications: random walks, branching processes, discrete Markov chains and, time permitting, the Poisson process and Brownian motion.
授業の目的と到達目標
／Objectives and attainment
goals		 The student will get acquainted with mathematical tools and techniques, as well as the probabilistic intuition necessary for understanding and successful use of stochastic models in a variety of applications within mathematics and in science, engineering, economics, etc. He/she will also learn how to build new models, in yet-unencountered situations and novel frameworks.
授業スケジュール
／Class schedule		 1 Probability Review
2 Stochastic Processes
3 Simple Random Walk: Theory
4 Simple Random Walk: Implementation
5 Random Walks - Advanced Methods
6 Kalman Filter
7 Branching Processes
8 Markov Chains: Theory  
9 Markov Chains: Implementation
10 Classification of States
11 Absorption and reward
12 Stationary and Limiting Distributions
13 Robotic applications
14 Wrap up
教科書
／Textbook(s)		 None, lecture notes will be delivered in classes.
成績評価の方法・基準
／Grading method/criteria		 By three assignments.
履修上の留意点
／Note for course registration		 None.
参考(授業ホームページ、図書など)
／Reference (course
website, literature, etc.)		 LMS.


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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;		 1年 , 2年
単位数
／Credits		 2.0
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
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		 Human-centered computing (HCC) is the science of decoding human behavior. It discusses a computational approaches to understand human behavior all aspects of human beings. However, the complexity of this new domain necessitates alterations to common data collection and modeling techniques. This course covers the techniques that underlie the state-of-the-art systems in this emerging field. Students will develop a critical understanding of human-centered computing including fundamentals, approaches and applications/services.
授業の目的と到達目標
／Objectives and attainment
goals		 This course aims at instructing our students (especially master students) the fundamentals of human-centered computing. Through this course, students are expected to:

1) cultivate interdisplinary thinking skills;
2) be able to build systems that combine technologies with organizational designs;
3) understand the human, and translate the human needs to real-world systems.
授業スケジュール
／Class schedule		 This course will give an introduction that covers a wide range of theories, techniques, applications to the well-designed human-computer systems.

Examples and academic papers on HCC researches (to each topics above) will be distributed to students who are enrolled in this class. Students will be grouped and make presentation on distributed research papers, and trained to ask questions and provide personal opinions on specific studies.

The schedule may be adjusted according to actual conditions. In that case, we will contact you separately. Remote / Online course may be conducted if necessary.
教科書
／Textbook(s)		 No specific textbook will be used for this course. Slides and handouts will be prepared by instructors, according to the references (see below), and available on the course website for download. Papers, news, and videos related to the theme will be selected from top-rank academic publications (e.g., IEEE/ACM/IEICE Transactions and SCI-indexed peer-reviewed journals), and the Internet (e.g., with copyright permission) will be taught during the classes.
成績評価の方法・基準
／Grading method/criteria		 Presentation: 30%
Report: 70%

Please note that the proportion of above grading criteria may be adjusted according to actual conditions.
履修上の留意点
／Note for course registration		 All the topics will be taught through the reading of past high quality studies. Students are expected to cultivate independent reading ability. The reports and the attendance are also considered very important factors to evaluate the learning performance.
参考(授業ホームページ、図書など)
／Reference (course
website, literature, etc.)		 Witold Pedrycz, Fernando Gomide (2007). Fuzzy Systems Engineering: Toward Human-Centric Computing
Wiebe Bijker, Of Bicycles, Bakelites, and Bulbs: Toward a Theory of Sociotechnical Change (Inside Technology)
Clifford Geertz, The Interpretation Of Cultures (Basic Books Classics) The Presentation of Self in Everyday Life
Victor Kaptelinin and Bonnie Nardi, Acting with Technology: Activity Theory and Interaction Design
Guy A. Boy (2011). The Handbook of Human-machine Interaction: A Human-centered Design Approach


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開講学期
／Semester		 2024年度／Academic Year 　3学期 ／Third Quarter
対象学年
／Course for;		 1年 , 2年
単位数
／Credits		 2.0
責任者
／Coordinator		 ハミード　サジ
担当教員名
／Instructor		 ハミード　サジ, 中里　直人
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 provides an introduction to parallel computing including
parallel architectures and parallel programming techniques.
授業の目的と到達目標
／Objectives and attainment
goals		 The students will learn the basic parallel programming models including
shared
memory and distributed memory models. Parallel programming using MPI and
OpenMP will be a main focus.  
The course will heavily involve coding projects and weekly assignments.
授業スケジュール
／Class schedule		 1. Introduction to Parallel Architecture
2. Introduction to Parallel Programming
3. Performance considerations
4. Programming in MPI
5. Programming in OpenMP
教科書
／Textbook(s)		 Numerical Analysis for Engineers and Scientists, G. Miller, Cambridge University Press
Parallel Programming with MPI, P.S. Pachebo, Morgan Kaufmann Publishers
Parallel Programming in C with MPI and OpenMP, M. J. Quinn, McGraw-Hill
成績評価の方法・基準
／Grading method/criteria		 Assignments = 30%, Project = 20%, Exam = 50%
履修上の留意点
／Note for course registration		 Computer architecture, mathematics, algorithms and programming
Students are expected to have good skills in C or Fortran programming to take this course.
参考(授業ホームページ、図書など)
／Reference (course
website, literature, etc.)		 http://pages.tacc.utexas.edu/~eijkhout/istc/istc.html


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開講学期
／Semester		 2024年度／Academic Year 　1学期 ／First Quarter
対象学年
／Course for;		 1年 , 2年
単位数
／Credits		 2.0
責任者
／Coordinator		 三瓶　岳昭
担当教員名
／Instructor		 三瓶　岳昭
推奨トラック
／Recommended track		 －
先修科目
／Essential courses		 －
更新日／Last updated on 2024/01/11
授業の概要
／Course outline		 This course provides fundamental knowledge of fluids, dynamics of a fluid flow and basic methods to obtain a numerical solution of the governing equations of fluid dynamics, for students studying fluid mechanics or related disciplines such as meteorology and physical oceanography.
(Note: This course does not deal with graphics or visualization of a fluid flow.)
授業の目的と到達目標
／Objectives and attainment
goals		 In this course, students will gain a basic understanding of fluid properties and principles of fluid dynamics, and learn how to solve simple problems of fluid dynamics through numerical integration of the governing equations.
授業スケジュール
／Class schedule		 1. Properties of fluids, Equation of state, Viscosity
2. Reynolds number, Similarity of flow, Fluid statics, Hydrostatic balance, Buoyancy
3. First law of thermodynamics, Potential temperature
4. Eulerian and Lagrangian descriptions of fluid motion, Equation of continuity
5. Euler's equation of motion, Bernoulli’s theorem and its applications
6. Vorticity and stream function, Divergence and velocity potential
7. Motion of viscous fluids, Navier-Stokes equation
8. Solutions of Navier-Stokes equation for some simple problems
9. Characteristics of 2nd-order partial differential equations, Discretization, Finite difference method
10. Accuracy and truncation error, Lax’s equivalence theorem, CFL condition, Numerical stability
11. von Neumann’s stability analysis, Amplification factor, Phase error
12. Numerical schemes for advection equation
13. Numerical schemes for diffusion equation
14. Numerical schemes for Laplace equation, Application of numerical methods to flow simulations
教科書
／Textbook(s)		 None.
成績評価の方法・基準
／Grading method/criteria		 Assignments and reports. (100%)
履修上の留意点
／Note for course registration		 Prerequisites (this course assumes understanding of the content of the following courses) are,
- MA03 & MA04 Calculus I & II (Undergraduate course)
- NS01 Dynamics (Undergraduate course)
- FU11 Numerical analysis (Undergraduate course)
Note: CSC08A “Numerical modeling and simulations” may be useful to understand the subject of this course.


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開講学期
／Semester		 2024年度／Academic Year 　3学期 ／Third Quarter
対象学年
／Course for;		 1年 , 2年
単位数
／Credits		 2.0
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
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		 Pattern mining is an important knowledge discovery technique in big data analytics. It involves identifying all regularities that exist in a database. Several algorithms were described in the literature to find user interest-based patterns that exist in a database.

In this course, students will learn several algorithms to discover hidden patterns that exist in transactional databases, temporal databases, and spatiotemporal databases.
授業の目的と到達目標
／Objectives and attainment
goals		 The main objective of this course is to empower the students with different pattern mining algorithms so that can select an appropriate algorithm to discover useful knowledge in large databases.  
授業スケジュール
／Class schedule		 Lecture topics:
1. Introduction to Pattern Mining – 1
2. Discovering frequent patterns in big data-1
a. Frequent pattern model
b. Search space
c. Apriori property
d. Apriori algorithm
3. Discovering frequent patterns in big data-2
a. ECLAT
b. FP-growth
4. Discovering periodic-frequent patterns in big temporal databases
a. Periodic-frequent pattern model
b. Mining Algorithms
5. Discovering partial periodic patterns in big temporal databases
6. Discovering fuzzy frequent patterns in non-binary transactional databases
7. Discovering fuzzy periodic frequent patterns in non-binary temporal databases
8. High utility pattern mining
9. Spatial high utility pattern mining
10. Rare Item Problem
11. User interest-based pattern mining – 1
a. Top-k patterns
b. Closed patterns
c. Maximal patterns
12. User interest-based pattern mining – 2
a. Frequent pattern mining in uncertain data
13. Pattern mining in streams

Exercise topics:
1. Understand datasets and using the algorithms in SPMF and PAMI_PyKit
2. Using Apriori algorithms to find desired patterns in the data
3. Using ECLAT and FP-growth algorithm to find frequent patterns
4. Finding hidden temporal patterns in big data using PFP-growth algorithm
5. Finding hidden temporal patterns in big data using using 3P-growth algorithm
6. Discovering hidden fuzzy frequent patterns in non-binary transactional databases.
7. Discovering hidden fuzzy periodic-frequent patterns in non-binary temporal databases
8. Implementing HUIM algorithm
9. Implementing SHUIM algorithm
10. Implementing MSApriori algorithm
11. Implementing closed and maximal pattern mining algorithms
12. Implementing frequent pattern mining algorithm for uncertain data
13. Implementing FP-stream algorithm
教科書
／Textbook(s)		 Data Warehousing and Mining: Han and Kamber and
Research papers
成績評価の方法・基準
／Grading method/criteria		 Students will be graded based on exercises and final exam. Exercises carry 75% of weightage and final exam carries 25% of weightage.

履修上の留意点
／Note for course registration		 Students must be good at programing. (Proficiency in Java/Python is preferable)


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開講学期
／Semester		 2024年度／Academic Year 　4学期 ／Fourth Quarter
対象学年
／Course for;		 1年 , 2年
単位数
／Credits		 2.0
責任者
／Coordinator		 蘇　春華
担当教員名
／Instructor		 蘇　春華, 可知　靖之
推奨トラック
／Recommended track		 －
先修科目
／Essential courses		 MA01–2 Linear Algebra I, II (undergraduate), MA03–4 Calculus I, II (undergraduate), or equivalent.
更新日／Last updated on 2024/02/01
授業の概要
／Course outline		 このコースは、暗号学者と純粋数学者が共同で教えるユニークなコースです。前半は可知（数学者）、後半は蘇（暗号学者）が教えます。

数学的な面では、純粋数学の中でも、ポスト量子暗号への応用を視野に入れ、（ライバル大学院の数学科に匹敵するような）質の高いトピックを厳選しています。ヤコビ和は、大整数の原始性検定（à la Adleman-Pomerance-Rumely）に応用できることが証明されました。後者は、前量子公開鍵アルゴリズムであるにもかかわらず、RSAにおいて非常に重要である。我々は当然、ヤコビ和の理論を再調整し、特に「ポスト」量子の文脈で重要な暗号化アルゴリズムを生み出す可能性があることを期待するものである。このような精神で、離散フーリエ解析の考えを取り入れて、統一根の和（ガウス和、ヤコビ和）の理論を取り上げます。

また、暗号学の分野では、ポスト量子暗号を目指した講義を行います。暗号技術入門（暗号、セキュリティ、公開鍵暗号、計算複雑性）、ポスト量子暗号入門、q線型暗号方式、多変数2次暗号方式、格子型暗号方式、アイソジェニー型暗号方式、量子フーリエ解析など。
授業の目的と到達目標
／Objectives and attainment
goals		 1. 数学と暗号学は、ともに純粋に理論的で、演繹的推論に基づくという点で異なる学問分野であることを理解すること。暗号学が純粋数学の原理を最も基本的な形で用いていることを理解する。
2. 統一根とその和の基本を理解する。不変量説を取り入れた枠組みを理解する。3. ポスト量子暗号（量子コンピュータの登場）の存在意義について理解する。4. ポスト量子暗号の基本を理解し、最先端レベルの暗号研究分野で何が問題になっているのかを理解する。
授業スケジュール
／Class schedule		 第1部

1. はじめに

-基本的な数論と公開鍵暗号方式


2. フーリエ変換等（古典的フーリエ解析）のクラッシュコース。

-フーリエ変換の枠組みで微分の公理的定義を再検討し、分数微分を視野に入れる。

-多項式環に作用する一般的な線形群。
多項式環に作用する一般的な線形群 -有限群作用による多項式環の不変部分環
-Galois理論に関する若干の小技。
-テンソル表現（クロネッカーのテンソル積）。

-アフィン空間の有限 abelian cover ガウス-ヤコビ和の理論とよくかみ合うガロア群から生じるもの。

3. 量子コンピュータ
- 量子コンピュータの基礎
- Shorのアルゴリズム
- 有限体演算
- カラツバと数論的変換に基づく乗算
- モンゴメリ乗算

第II部

4. 格子暗号
- 格子暗号の基礎知識
- NewHope, Kyber, Saber (NISTのポスト量子候補)
- NTRU、NTRU Prime
- デジタル署名アルゴリズム  Dilithium、Falcon

5. コードベース暗号
- クラシックマッケイリス
- HQC

6. アイソジェニー・ベースト暗号
- 超重合同型鍵交換(SIKE)
- 同質性に基づくデジタル署名アルゴリズム
教科書
／Textbook(s)		 Post-Quantum Cryptography by Daniel J. Bernstein, Johannes Buchmann, Erik Dahmen
https://link.springer.com/book/10.1007/978-3-540-88702-7
成績評価の方法・基準
／Grading method/criteria		 成績評価は、レポート（50％）と最終発表（50％）に基づいて行われます。
履修上の留意点
／Note for course registration		 前提条件 MA01-2 線形代数I, II（学部）, MA03-4 微積分I, II（学部）, または同等。
参考(授業ホームページ、図書など)
／Reference (course
website, literature, etc.)		 References in https://pqcrypto.org


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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;		 1年 , 2年
単位数
／Credits		 2.0
責任者
／Coordinator		 中村　章人
担当教員名
／Instructor		 中村　章人, 渡邊　曜大, 蘇　春華
推奨トラック
／Recommended track		 －
先修科目
／Essential courses		 －
更新日／Last updated on 2024/01/25
授業の概要
／Course outline		 Current computing environments include various kinds of endpoints like smart phones, portable/desktop PCs, server computers, IoT devices, and virtual machines on cloud computing platforms. To show the capabilities and performance of them and prevent accidents and cyberattacks, information security and management technologies for administrative work are must-have features.
    This course introduces concepts and mechanisms of computer and network security and management. We will also review several state-of-the-art real-world technologies and tools.
授業の目的と到達目標
／Objectives and attainment
goals		 - Acquisition of fundamental knowledge of theoretical and practical security.
- Acquisition of basic skill and knowledge to administrate ICT systems security.
授業スケジュール
／Class schedule		 1, 2. Fundamentals
- Goal of information security
- Risk, threat, vulnerability, and control
- Confidentiality, integrity, availability (C-I-A) triad
- Attack paradigm and protection paradigm
- Authentication and password

3, 4. Vulnerability
- Classes of vulnerabilities
- Trends of vulnerability
- Vulnerability management
- Open standards for vulnerability management

5, 6. Cryptography for IoT Security and Privacy
- Cryptography for IoT
- Side channel attacks
- Authentication by IoT

7, 8. System security
- OS security
- Password and access control
- FinTech security

9, 10. Security notions for public key encryption schemes
- Definition of public key encryption schemes
- Security goals: Semantic security (SS), Indistinguishability (IND), Non-malleability (NM)
- Attacking models: Chosen plaintext attack (CPA), Chosen ciphertext attack (CCA1, CCA2)

11, 12. Relation among security notions
- Equivalence between SS and IND
- NM implies IND, IND-CCA2 implies NM-CCA2

13, 14. Cyberattacks and controls
- DoS and DDoS attacks
- Password cracking
- Risks of Web applications
教科書
／Textbook(s)		 No text book.
Teaching materials will be distributed in the class.
成績評価の方法・基準
／Grading method/criteria		 Method: assignments (reports)

Criteria:
- Correctness for computational problems
- Relevance, quality, presentation and originality for essays
履修上の留意点
／Note for course registration		 The course assumes a basic knowledge of mathematical logic and probability.
参考(授業ホームページ、図書など)
／Reference (course
website, literature, etc.)		 The course instructor Akihito Nakamura has practical working experience: He worked for AIST (National Institute of Advanced Industrial Science and Technology) for 20 years where he was involved in R&D of information security and cloud computing. Based on his experience, he equips students with the advanced technical knowledge of information security.


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開講学期
／Semester		 2024年度／Academic Year 　4学期 ／Fourth Quarter
対象学年
／Course for;		 1年 , 2年
単位数
／Credits		 2.0
責任者
／Coordinator		 李　想
担当教員名
／Instructor		 李　想, 蘇　春華
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		 Signal processing is one of the fundamental theories and techniques for constructing modern information systems. During the last half century, many theories and methods have been proposed and widely studied for signal processing. In this core course, first, we review the fundamentals of discrete-time signals and systems. The related content includes the concept and the classification of discrete-time signal, representations of signals in time, frequency, z- and discrete frequency domains, representations and analyses of systems, and filter designs. Then we focus more on stochastic signals/systems, besides of deterministic signals/systems, which is the main topic of the undergraduate course “Signal Processing and Linear System”. Topics include estimation theory, random signal modeling, characterization of stochastic signals and systems, nonparametric estimation, adaptive signal processing, and Kalman filtering.
授業の目的と到達目標
／Objectives and attainment
goals		 This course is designed as a fundamental common core course for graduate school students studying in all the fields of information system. It presents a methodology for fundamental signal processing, and then introduce some advance topics, especially statistical features of the signal, and how to apply them for more sophisticated signal processing. Furthermore, the course presents implementation of the methods for signal processing with computer programming. Finally, the course provides some applications, such as noise canceling, system identification, Kalman filter etc.
授業スケジュール
／Class schedule		 1) Introduction
2) Linear, time-invariant systems, impulse response and convolution sum
3) Fourier transform, frequency response and sampling theorem
4) The z-transform and its properties, the inverse z-transform
5) Differential equation, transfer function and system stability
6) Discrete Fourier transform (DFT), Fast Fourier transform (FFT)
7) Finite Impulse Response (FIR) filters and Infinite Impulse Response (IIR) filters
8) Fundamentals of discrete-time signal processing
9) Random variables, sequences, and stochastic process
10) Spectrum estimation
11) Optimum linear filters, Wiener filter
12) Least-squares filtering and prediction, adaptive filters
13) Algorithms and structures for optimum linear filters
14) Kalman filter
教科書
／Textbook(s)		 [1] Lecture notes prepared by the instructor.
[2] Applied Digital Signal Processing: Theory and Practice. Dimitris G Manolakis and Vinay K. Ingle. Cambridge University Press, 2011, ISBN: 9780521110020.
[3] Signals and Systems (2nd Edition), Alan V. Oppenheim, Alan S. Willsky and S. Hamid, Pearson, 1997, ISBN: 0138147574.
成績評価の方法・基準
／Grading method/criteria		 Homeworks (35%)
Quizzes (15%)
Final Report (50%)
履修上の留意点
／Note for course registration		 Online Class: Available
参考(授業ホームページ、図書など)
／Reference (course
website, literature, etc.)		 [1] Applied Digital Signal Processing: Theory and Practice. Dimitris G Manolakis and Vinay K. Ingle. Cambridge University Press, 2011, ISBN: 9780521110020.
[2] Signals and Systems (2nd Edition), Alan V. Oppenheim, Alan S. Willsky and S. Hamid, Pearson, 1997, ISBN: 0138147574.
[3] Statistical and Adaptive Signal Processing, Dimitris G. Manolakis et al. Boston: McGraw-Hill, 2000, ISBN: 163081203X.


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開講学期
／Semester		 2024年度／Academic Year 　3学期 ／Third Quarter
対象学年
／Course for;		 1年 , 2年
単位数
／Credits		 2.0
責任者
／Coordinator		 土屋　貴裕
担当教員名
／Instructor		 土屋　貴裕, 橋本　康弘, 渡邊　曜大
推奨トラック
／Recommended track		 －
先修科目
／Essential courses		 －
更新日／Last updated on 2024/01/15
授業の概要
／Course outline		 This course provides advanced contents of applied statistics. Most important statistical methods are explained with many examples of data. At the same time, their mathematical foundations are given.
授業の目的と到達目標
／Objectives and attainment
goals		 Students can understand basic applied statistics such as estimation, test, regression, and analysis of variance by using Gaussian, t, F, and chi-square distributions. Moreover, they can learn knowledge on stochastic processes.
授業スケジュール
／Class schedule		 The course contains following topics with 3-4 hours for each including lecture and practical exercises.

Topic 1. Basic Math:
- A fundamental set theory
- Improper integral

2. Events and Probabilities
- The intuitive meaning of Probability model;
Sample space, point of omega, Event as measurable set, the probability of an event.
- measurable and sigma algebra
- measure theory and probability triple

3. Random Variables and Probability Distributions
- Random variables
- Distribution functions; pmfs, pdfs
- A first transformation rule for pdfs
- Expectation \$ |mathbb $\{E\} \$$
- Variance and Covariance
- Integrable space $ L^ p $
- Correlation coefficient, MSE
- Variance of a sum

4. Conditioning and Independent
- Red and white balls
- sample space, prior probabilities likelihood
- Bayes' theorem
- Red and White ball example again*.
- Independence
- Law of large numbers
- Unbiased Estimator of Sample Variance.
- Using the sample median quantiles in statistics
- Strong Law of Large Numbers
- Empirical Distribution Function


5. Generating function and the central limit theorem
- Generating Functions (GFs)
- The central limit theorem
- Under a suitable moment conditions
- Statement of Central limit theorem
- Beyond the moment condition
- An application: Regression
- Linear least squares: ordinary least squares
- The true coefficient via Central limit theorem
- L2-norm Ridge and L1-norm Lasso
教科書
／Textbook(s)		 Class materials will be distributed.
成績評価の方法・基準
／Grading method/criteria		 Reports 50％, Quiz 50%.
履修上の留意点
／Note for course registration		 Courses preferred to be learned prior to this course :
Calculus, Linear algebra, Probability, Information theory.
参考(授業ホームページ、図書など)
／Reference (course
website, literature, etc.)		 Weighing the Odds: A Course in Probability and Statistics (English Edition)
W. Feller, An Introduction to Probability Theory, Vol.1, (Wiley)


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開講学期
／Semester		 2024年度／Academic Year 　1学期 ／First Quarter
対象学年
／Course for;		 1年 , 2年
単位数
／Credits		 2.0
責任者
／Coordinator		 山上　雅之
担当教員名
／Instructor		 山上　雅之, 浅井　信吉
推奨トラック
／Recommended track		 －
先修科目
／Essential courses		 －
更新日／Last updated on 2024/01/14
授業の概要
／Course outline		 This course provides basic knowledge of quantum information and quantum computations for graduate students who want to learn modern information and computational models.
授業の目的と到達目標
／Objectives and attainment
goals		 At the end of the course, students can acquire
1) Basin knowledge of quantum mechanics for quantum information theory
2) Algorithms of quantum computers
3) Quantum cryptography.
授業スケジュール
／Class schedule		 First part (Prof. YAMAGAMI Masayuki)
1: Introduction: Purpose of quantum information
2: Review of quantum mechanics 1 (Quantum systems)
3: Review of quantum mechanics 2 (Interference of quantum states)
4: Definition of quantum computer
5: Elementary quantum algorithm 1 (Deutsch-Jozsa algorithm)
6: Elementary quantum algorithm 2 (Grover's algorithm)
7: Summary and guidance for further study

Second part (Prof. ASAI Nobuyoshi)
8: Quantum state and vector expression
9: Quantum operation and Matrix Expression
10: Superposition, entanglement, and measurement
11: Quantum Fourier transform
12: Phase Estimation
13: Harrow-Hassidim-Lloyd(HHL) algorithm
14: Summary
教科書
／Textbook(s)		 No specific textbooks are designated, but the following are usefull books for beginners.
1. 量子コンピュータ ― 超並列計算のからくり(竹内 繁樹、講談社, in Japanese)
2j. 量子情報理論(佐川弘幸 / 吉田宣章, 丸善出版, 日本語版, in Japanese)
2e. Fundamentals of Quantum Information(H. Sagawa and N. Yoshida, World Scientific, in English)
3.j みんなの量子コンピュータ （クリス バーンハルト [(翻訳) 湊 雄一郎, 中田 真秀] 、翔泳社, in Japanese）
3.e Quantum Computing for Everyone (Chris Bernhardt, The MIT Press, in English)
成績評価の方法・基準
／Grading method/criteria		 Reports: 50 points (first part) and 50 points (second part)
履修上の留意点
／Note for course registration		 A basic proficiency in linear algebra is desired.
It is recommended to have basic knowledge of quantum mechanics.
However, the course will accept students who do not have any knowledge of quantum mechanics but have strong desire to learn new knowledge.


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開講学期
／Semester		 2024年度／Academic Year 　1学期 ／First Quarter
対象学年
／Course for;		 1年 , 2年
単位数
／Credits		 2.0
責任者
／Coordinator		 鈴木　大郎
担当教員名
／Instructor		 鈴木　大郎, 渡邊　曜大
推奨トラック
／Recommended track		 －
先修科目
／Essential courses		 －
更新日／Last updated on 2024/01/25
授業の概要
／Course outline		 計算は計算機科学において最も重要な概念である。計算機の能力に限界があるという、計算機科学と計算機工学に関わる者が必須の事柄も、計算の概念によって示すことができる。
計算の概念によって、我々が計算機に解かせたい問題は計算可能と計算不能なものという２つのクラスに分類される。後者はけして計算機で解くことはできない。
この授業では、計算の厳密な概念である計算モデルとそれらの間の等価性、および計算不能な問題の存在とその具体例について学ぶ。
授業の目的と到達目標
／Objectives and attainment
goals		 この授業を履修した学生は、チューリング機械、レジスタ機械、帰納的関数のような種々の計算モデルについての知識を得ることができる。さらに、計算機の限界、すなわり計算機で解くことのできない問題が存在することを理解し、計算機で解くことのできない問題にはどのようなものがあるかを述べることができる。
授業スケジュール
／Class schedule		 第1回
1. 計算可能性と計算モデルの概要

第2回から第7回
2. 計算モデル
2.1 チューリング機械モデル
2.2 ランダムアクセスモデル (RAM)
2.3 帰納的関数モデル
2.4 Whileプログラムモデル

第8回から第10回
3. チャーチチューリングの提唱
3.1 計算モデルの等価性
3.2 チャーチチューリングの提唱
3.3 自然数以外のデータ型での計算

第11回と第12回
4. 万能プログラム
4.1 プログラムのコード化
4.2 万能プログラムの構成

第13回と第14回
5. 計算不能な問題
5.1 停止問題
5.2 帰着可能性

各回の授業とその内容との対応は、授業の進み具合により変更になることがある。
教科書
／Textbook(s)		 教科書は指定しない。授業の中で配布するハンドアウトに沿って授業を進める。
成績評価の方法・基準
／Grading method/criteria		 評価は最後の授業終了後メールで配布される最終課題の提出による。履修者は最終課題の解答を指定された期日までに返信しなければならない。
また、授業中に演習問題を数問解いてもらう。演習問題は成績評価には含まれないが、解かなければ最終課題に解答することが難しくなるので、演習問題を授業中に必ず解かなければならない。
履修上の留意点
／Note for course registration		 この授業を履修する学生は、学部の授業である F3 離散系論、F1 アルゴリズムとデータ構造I、F8 オートマトンと言語理論、M9 数理論理学で学ぶ内容を知っていることが望ましい。
参考(授業ホームページ、図書など)
／Reference (course
website, literature, etc.)		 R. Sommerhalder, S.C. van Westrhenen. The theory of computability : programs, machines, effectiveness and feasibility. Addison-Wesley. 1988.

Martin D. Davis, Ron Sigal, Elaine J. Weyuker. Computability, complexity, and languages : fundamentals of theoretical computer science. Academic Press. 1994.

Douglas S. Bridges. Computability : a mathematical sketchbook. Springer. 1994.

Carl H. Smith. A recursive introduction to the theory of computation. Springer. 1994.

Tom Stuart. Understanding computation : from simple machines to impossible programs. O'Reilly.  2013.
(Tom Stuart著; 笹井崇司訳. アンダースタンディングコンピュテーション: 単純な機械から不可能なプログラムまで. オライリー・ジャパン. 2014)

Chris Hankin. Lambda calculi: a guide for computer scientists.
Oxford University Press. 1994.

五十嵐善英, Forbes D.Lewis, 舩田眞里子著. 計算理論入門 (Introduction to computation theory). 牧野書店. 2013.

A.クフォーリ, R.モル, M.アービブ共著; 甘利俊一, 金谷健一, 川端勉共訳. プログラミングによる計算可能性理論. サイエンス社. 1987.

高橋正子著. 計算論 : 計算可能性とラムダ計算. 近代科学社. 1991.


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;		 1年 , 2年
単位数
／Credits		 2.0
責任者
／Coordinator		 趙　強福
担当教員名
／Instructor		 趙　強福, 劉　勇
推奨トラック
／Recommended track		 －
先修科目
／Essential courses		 (not absolutely needed) Artificial intelligence (under graduate course)
更新日／Last updated on 2024/01/24
授業の概要
／Course outline		 Most (if not all) engineering problems can be formulated as optimization problems. Even machine learning (e.g. training of a neural network, finding the best architecture of a deep neural network for image classification) is a special case of optimization. To solve optimization problems, different methods have been studied in mathematical programming, operations research, and so on. Conventional methods, however, are usually not efficient enough when the problem space is large and complex. Many problems faced in artificial intelligence are combinatorial optimization problems. These problems are NP-hard, and we may never find polynomial time solutions. To solve these problems efficiently, different "heuristics" have been used to "search for sub-optimal solutions".

Heuristics are search methods produced based on human intuition and creative thinking, or inspired by some natural phenomena. They are often useful for finding good local solutions quickly in a restricted area. Meta-heuristics are multi-level heuristics that can control the whole process of search, so that global optimal solutions can be obtained systematically and efficiently. Although meta-heuristics cannot always guarantee to obtain the true global optimal solution, they can provide very good results for many practical problems. Usually, meta-heuristics can enhance the computing power of a computer system greatly without increasing the hardware cost.

So far, many meta-heuristics have been proposed in the literature. In this course, we classify meta-heuristics into two categories. The first one is "single-point" (SP) search, and the second one is "multi-point" (MP) search. For the former, we study tabu search, simulated annealing, iterated local search, and so on. For the latter, we study evolutionary algorithms, including genetic algorithms, genetic programming, evolutionary strategy, and memetic algorithm; ant colony optimization, and particle swarm optimization. Although the efficiency and efficacy of these methods have been proved through experiments, because they were proposed based on human intuition, the theoretic foundation is still weak. Therefore, in this course, we will mainly introduce the basic idea of each method, and try to explain the physical meaning clearly. Mathematical proofs will be introduced very briefly when necessary.
授業の目的と到達目標
／Objectives and attainment
goals		 In this course, we will study the following topics:
(1) Examples of important optimization problems.
(2) Conventional optimization methods.
(3) Single-point (SP) search methods:
* Tabu search.
* Simulated annealing.
* Iterated local search.
* Guided local search.
(4) Multi-point (MP) search methods
* Genetic algorithm (GA).
* Genetic programming (GP).
* Evolutionary programming (EP).
* Memetic algorithm (MA).
* Differential evolution (DE).
* Particle swarm optimization (PSO) and ant colony optimization (ACO).

After this course, students should be able to
(1) Understand the basic ideas of each meta-heuristics algorithm;
(2) know how to use meta-heuristics for solving different problems; and
(3) become more interested in developing new algorithms.
授業スケジュール
／Class schedule		 (1) An Introduction to Optimization
- Classification and Case Study.

(2) An Introduction to Optimization
- An Brief Review of Conventional Search Algorithms.

(3) Tabu search
- Tabu list, intensification, and diversification.

(4) Simulated annealing
- Find the global optimum without remembering search history.

(5) Iterated local search and guided local search
- Strategies for repeated search.

(6) Team work I
- Solving problems using single point search algorithms.

(7) Presentation of team work I.

(8) Genetic algorithm
- Basic components and steps of GA.

(9) Other Evolutionary Algorithms
- Evolution strategies, evolutionary programming, and genetic programming.

(10) Differential evolution
- Evolve more efficiently, but why?

(11) Memetic algorithms
- Meme, memotype, memeplex, and memetic evolution.
- Combination of memetic algorithm and genetic algorithm.

(12) Swarm Intelligence
- Ant colony optimization
- Particle swarm optimization

(13) Team work II
- Solving problems using multi-point search algorithms

(14) Presentation of team work II.

IMPORTANT: Depends on the situation of the world pandemic, we may conduct the lectures online, to avoid spreading of coronavirus. The ID and PW for each lecture will be sent to the students before the lecture. Please do not forward them to anyone not related to this course.  

REMARK: You can also come to the class room to attend the face-to-face lecture ONLY IF you have difficulty to attend the online lectures.
教科書
／Textbook(s)		 There is no text book. Teaching materials will be distributed in the class.
成績評価の方法・基準
／Grading method/criteria		 - Quiz: 30 points.
- Team works: 70 (35 x 2) points.
- Active Participation will also be considered in evaluation
履修上の留意点
／Note for course registration		 This course is related to "optimization" or "search". If you are interested in knowing how to find (search for) the "best" solution for any given problems, effciently and effectively, please take this course. Note that designing deep neural networks, electronic devices, and so on, are all "special cases" of optimization problems.

参考(授業ホームページ、図書など)
／Reference (course
website, literature, etc.)		 1) M. Gendreau and J. Y. Potvin, Handbook of metaheuristics, 2nd Edition, Springer, 2010.

2) C. Cotta, M. Sevaux, and K. Sorensen, Adaptive and multilevel metaheuristics, Springer, 2010.

3) URL of this course: http://www.u-aizu.ac.jp/‾qf-zhao/TEACHING/MH/mh.html


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;		 1年 , 2年
単位数
／Credits		 2.0
責任者
／Coordinator		 浅井　和人
担当教員名
／Instructor		 浅井　和人, 橋本　康弘, 可知　靖之, 土屋　貴裕, 渡邊　曜大
推奨トラック
／Recommended track		 －
先修科目
／Essential courses		 －
更新日／Last updated on 2024/01/19
授業の概要
／Course outline		 Course implementation methods: Remote classes

A graph, composed of vertices and edges, is one of the most fundamental objects in mathematics. In spite of its simple definition, tons of notions concerning graphs are introduced, and it is sometimes very laborious to perform complete introduction of graph theory. In this class, we first overview graph theory terminology to moderate extent, then we change the focus to carefully selected important topics, and advance our knowledge in that area. For example, we focus on vertex/edge connectivity, and introduce Menger's theorem and Mader's theorem; also focus on spanning trees and Kirchhoff's theorem, etc.

Graph theory, as a branch of mathematics, growing its branches like a tree, and even at present, contains many difficult open problems. As another aspect, it has a lot of applications to several areas. Graphs can be used to model many types of relations and processes in physical, biological, social and information systems. This is a reason why graph theory is important for many people in wide areas.
授業の目的と到達目標
／Objectives and attainment
goals		 Graphs, Subgraphs, Isomorphic graphs, Degrees of vertices, Walks, Trails, Paths, Distance, Diameter, Coloring, Special graphs, Multigraphs and matrices, Eulerian/Hamiltonian multigraphs, Connectivity, Menger's theorem, Mader's theorem, Planarity (optional), Trees, Spanning trees, Kirchhoff's theorem, Deletion-contraction method, Cayley's formula, Minimum spanning trees, Decompositions of graphs.
授業スケジュール
／Class schedule		 1--2. Definition and basics
3. Walks, trails, paths; Connectivity
4. Distance and diameter; Coloring
5. Special graphs, matrices
6. Eulerian/Hamiltonian multigraphs
7. Connectivity (revisited)
8. Menger's theorem, Mader's theorem
9. Trees, Spanning trees and Kirchhoff's theorem
10. Deletion-contraction method
11. Prufer's bijective proof of Cayley's formula
12. Minimum spanning trees
13. Decomposition of graphs
14. Gyarfas tree packing conjecture
教科書
／Textbook(s)		 1. Handout: A Graduate Text for the Core Course: -- Graph Theory --, by K. Asai

2. Graph Theory (Graduate Texts in Mathematics, Vol. 173) (2012), Springer, by R. Diestel

3. Pearls in Graph Theory: A Comprehensive Introduction (Dover Books on Mathematics) (2003), Dover Publications, by N. Hartsfield, G. Ringel
成績評価の方法・基準
／Grading method/criteria		 Report:80％
Presentation:20%
履修上の留意点
／Note for course registration		 Related courses: Discrete Systems, Algorithms and Data Structures
参考(授業ホームページ、図書など)
／Reference (course
website, literature, etc.)		 Home page for the class: http://web-ext.u-aizu.ac.jp/~k-asai/classes/class-texts.html


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;		 1年 , 2年
単位数
／Credits		 2.0
責任者
／Coordinator		 中里　直人
担当教員名
／Instructor		 中里　直人, 浅井　信吉, 藤本　裕輔
推奨トラック
／Recommended track		 －
先修科目
／Essential courses		 －
更新日／Last updated on 2024/01/25
授業の概要
／Course outline		 This course mainly introduces

1. Ordinary and partial differential equations appear in science or engineering
2. Schemes to discretize the differential equations
3. Computational techniques to get numerical solutions.
4. Use of programming language and numerical libraries for solving differential equations and visualizing the results of simulation; main attention is focused on the use of C, Java, Python, or similar languages.

This course starts with the theory and mathematics of differential equations followed by hands-on style exercises as well as computer-related exercises on numerical techniques to solve various differential equations.
授業の目的と到達目標
／Objectives and attainment
goals		 The main goal of this course is to introduce the basic theory of differential equations and several most important numerical techniques and schemes to get solutions to those equations.

To program numerical solutions in exercise, we encourage students to use
1. Python for obtaining preliminary results with its efficient visualization and
2. C or Java languages for high-performance programs
3. Or other programming languages as you choose.
授業スケジュール
／Class schedule		 week 1 Introduction to Ordinary Differential Equations (N.Nakasato)
week 2 Floating-point arithmetic operations (N.Nakasato)
week 3 Introduction to Partial Differential Equations (N.Nakasato & N.Asai)
week 4 Topics in Partial Differential Equations (1) (N.Nakasato, N.Asai & Y.Fujimoto)
week 5 Topics in Partial Differential Equations (2) (N.Nakasato, N.Asai & Y.Fujimoto)
week 6 Practical Applications of Numerical Modeling and Simulations (N.Asai)
week 7 Practical Applications of Numerical Modeling and Simulations (Y.Fujimoto)
教科書
／Textbook(s)		 Modeling with Differential Equations, by D.Burghes & M.Borrie, Ellis Horwood Ltd , 1981

Partial Differential Equations for Scientists and Engineers, Stanley J. Farlow, Dover Publications, 1993
成績評価の方法・基準
／Grading method/criteria		 Homework&Reports (100 points)


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;		 1年 , 2年
単位数
／Credits		 2.0
責任者
／Coordinator		 可知　靖之
担当教員名
／Instructor		 可知　靖之, ヴィリエッタ　ジョヴァンニ
推奨トラック
／Recommended track		 －
先修科目
／Essential courses		 必修要件: MA01–2 線形代数 I & II (学部 1 年生向け) 、MA03–4 微積分 I & II (学部 1 年生向け) 、或いはそれと同等の単位と認定されるもの。MA05 フーリエ解析 (学部 2 年生向け) はいわゆる必修要件ではないが、受講歴があると望ましい。そうでなくとも、同科目についてある程度知識があれば理想的である。
更新日／Last updated on 2024/01/31
授業の概要
／Course outline		 このコースでは測度理論とルベーグ積分の初歩に焦点を当てます。 大学院生のみなさんは積分について詳しい筈ですが、そのみなさんの知っている積分は正確には「リーマン積分」と呼ばれます。 「ルベーグ積分」はリーマン積分を精密化したものです。 何の役に立つのか、ということを手短かに述べると、学部の『フーリエ解析』(MA05) の講義ではリーマン積分に基礎を置きつつ教えています (正当な理由があるため) が、実は、フーリエ解析の全体像はルベーグ積分に基礎を置いた理論展開により初めて開けてくるのです。数学的には、フーリエ解析の主な研究対象である二乗可積分函数の空間は、完備な無限次元ベクトル空間、いわゆるヒルベルト空間になります。 、ルベーグ測度に関して。 実は、数学の中の解析方面で使われる多くの定理・公式などは、最終的にはその事実に帰着します。 フーリエ解析はデータ サイエンス (信号処理、画像解析など) に必須の知識でもあるため、本講義内容は、データ・サイエンティストだけでなく、より広範にコンピュータ理工学関連分野に携わるすべての人にとって役立つと信じます 。 本講義は、全国の数学および理工系の学部・大学院を持つほとんどの大学で標準カリキュラムの一部として教えられている内容と一致しています。
授業の目的と到達目標
／Objectives and attainment
goals
L^1 空間と L^2 空間の完備性に重点を置き、測度理論とルベーグ積分の初歩を取り上げる。

目標:

1. 19 世紀に測度論とルベーグ積分の概念が姿を現したことが数学史における大きなパラダイム転換であったことを理解する。 今日、測定理論とルベーグ積分がコンピュータ理工学に浸透していることを理解する。

2. 測度理論の基礎を理解する : ボレルの σ 代数、σ 加法性 (σ 加法関数)、R^n のルベーグ測度、測度零集合。

3. ルベーグ積分の基礎を理解する。 L^1 空間の完備性がルベーグ積分の概念の存在意義を高めること。 同様に、二乗可積分性の概念と L^2 空間 (ヒルベルト空間) の完備性を理解する。

4. ルベーグ積分の概念がフーリエ解析の基礎づけにどのように寄与するかを理解する。
授業スケジュール
／Class schedule		 1. 長さ・面積・極限の概念の再検討。 一次元の間隔。
2. カントール集合。 σ-加法性 Part I. ペアノ・ジョルダン測度。
3. ルベーグの外測度・内測度。 測度零集合。
4.カラテオドリ/ルベーグ・スティルチェスの外測度。 可測集合。 ボレル σ-代数。
5. σ-加加性 Part II. 完備性。測度空間、ファトウの補題。
6. R^n のルベーグ測度。 R^n 内のボレル集合。 可測な cover/kernel 。
7. 連続函数 f に対する区間 I の逆像 f^−1 (I)。 ボレルの正規数。単函数。
8. 可測函数。 それらの和・積・積分。 エゴロフの定理。
9. ルベーグ積分、定義と基本性質。 ルベーグの支配収束定理。
10. ルーディンの定理。 L^p 空間。 リースの定理。 ハーン・ジョーダン分解。
11. ラドン・ニコディムの定理。 ヴィターリの補題。 σ-加法函数の微分。
12. フビニの定理。 ハウスドルフ測度/ハウスドルフ次元。
13. フーリエ解析への応用 I．
14. フーリエ解析への応用 II．
教科書
／Textbook(s)		 講義ノートを用いる。

参考書 :  

志賀浩二 『ルベーグ積分 30 講』数学 30 講シリーズ 9   朝倉書店
伊藤清三 『ルベーグ積分入門』 裳華房
成績評価の方法・基準
／Grading method/criteria		 試験と宿題。
履修上の留意点
／Note for course registration		 必修要件: MA01–2 線形代数 I & II (学部 1 年生向け) 、MA03–4 微積分 I & II (学部 1 年生向け) 、或いはそれと同等の単位と認定されるもの。MA05 フーリエ解析 (学部 2 年生向け) はいわゆる必修要件ではないが、受講歴があると望ましい。そうでなくとも、同科目についてある程度知識があれば理想的である。
参考(授業ホームページ、図書など)
／Reference (course
website, literature, etc.)		 参考書 :  

志賀浩二 『ルベーグ積分 30 講』数学 30 講シリーズ 9   朝倉書店
伊藤清三 『ルベーグ積分入門』 裳華房


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;		 1年 , 2年
単位数
／Credits		 2.0
責任者
／Coordinator		 渡部　有隆
担当教員名
／Instructor		 渡部　有隆, 西舘　陽平
推奨トラック
／Recommended track		 －
先修科目
／Essential courses		 －
更新日／Last updated on 2024/01/25
授業の概要
／Course outline		 Data structures play a key role in computer science and engineering. They are essential components to implement many efficient algorithms. This graduate-level course covers advanced topics not studied in introductory courses on algorithms and data structures. This course focuses on not only theory but also on practice to implement the advanced data structures and algorithms.

The lectures and exercises will be given online according to the situation.
授業の目的と到達目標
／Objectives and attainment
goals		 The core course covers several advanced data structures related to balanced search trees, range queries, sets and persistent data structures as well as advanced algorithms for graphs, string, computational geometry and artificial intelligence.  Students should seek to develop a solid understanding of common and practical data structures as well as techniques used in their implementation to solve real world problems.
授業スケジュール
／Class schedule		 1. Introduction: Review of fundamental data structures and algorithms as well as theory and techniques to analyze algorithms.
2. Balanced Tree: Basic Binary Search Trees, Treap, Red-Black Trees, Splay Trees, etc.
3. Range Query: Segment Trees, Range Minimum Query, Lazy Evaluation, Heavy-Light Decomposition, etc.
4. Sets: Union Find Trees, Merge Techniques, Persistent Data Structures, etc.
5. Algorithms on String: Suffix Arrays and Trees, Rolling Hash, Trie, etc.
6. Graph Algorithms: Bridges, Articulation Points, Max-Flow, Min-Cost-Flow, Bipartite Matching, etc.
7. Computational Geometry. Closest Pairs, Range Search, Sweep Algorithms, Segment Intersections, Voronoi Diagrams, etc.
8. Heuristic Search. Search Pruning, A*, Iterative Deepening, IDA*, etc.

It is subject to change, so some of these topics may be omitted and additional topics can be selected depending on the progress.
教科書
／Textbook(s)		 1. Introduction to Algorithms, Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest, and Clifford Stein.
2. Algorithm Design Manual, Steven S Skiena.
3. Algorithm Design, J. Kleinberg, E. Tardos
成績評価の方法・基準
／Grading method/criteria		 Assignments 50 %
Examinations 50 %
履修上の留意点
／Note for course registration		 • Reviewing undergraduate courses Algorithms and Dada Structures I and II is expected.
• The students should have basic skill of programming in C++ or Java.
参考(授業ホームページ、図書など)
／Reference (course
website, literature, etc.)		 https://onlinejudge.u-aizu.ac.jp/


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