2020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)

2021/01/30  現在

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開講学期
／Semester		 2020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		 －
履修規程上の先修条件
／Prerequisites 		－
使用言語
／Language		 －

更新日／Last updated on 2020/01/22
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 distributed in the class.
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		 No special prerequisite.
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		 2020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		 －
履修規程上の先修条件
／Prerequisites 		－
使用言語
／Language		 －

更新日／Last updated on 2020/01/22
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 solve some real world problems.

2. To understand the advantages and disadvantages of nature-inspired
   design 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		 1. Introduction
    Give an overview of evolutionary computation, and describe
    a number of evolutionary algorithms including genetic algorithms,
    evolutionary programming, evolution strategies, and genetic
    programming.

2. Basic Design by Evolutionary Optimization
    Function optimization appears in many applications. An example
    of developing a fast evolutionary programming is given
    to explain how to find a research problem, how to develop a new
    method, and how to evaluate the new method statistically.

3. Game Design by Evolutionary Learning
    Evolutionary algorithms can be used to learn game-playing
    strategies without human intervention.  Some fundamental
    questions are discussed, including how to learn a game
    without any teachers, how well the player can learn,
    and how well the evolved strategies can generalize.

4. Evolutionary Learning Systems
    Neural network design is a typical design problem because
    there is a learning component to it. Both direct encoding
    representations and indirect encoding representations
    are introduced in the lectures. An example of designing
    neural network ensembles will be discussed.

5. Rorobot Rontrollers by Evolvable Systems for
    Evolvable systems refer to the intelligent systems that can change their
    architectures and behaviors dynamically and autonomously
    by interacting with their environments.
    An example of evolvable system design for a robot controller
    is given. In this example, reinforcement learning will also be introduced.
教科書
／Textbook(s)		 On-line lecture notes will be available.
成績評価の方法・基準
／Grading method/criteria		 The students will be asked to design and simulate two evolutionary systems, 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		 CSA01 and ITC05 have covered some important concepts relevant to the course.
参考(授業ホームページ、図書など)
／Reference (course
website, literature, etc.)		 1. Online lectures will be provided.

2. A list of papers will be given after each lecture.

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


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開講学期
／Semester		 2020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		 －
履修規程上の先修条件
／Prerequisites 		－
使用言語
／Language		 －

更新日／Last updated on 2020/01/28
授業の概要
／Course outline		 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)		 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)
成績評価の方法・基準
／Grading method/criteria		 Final examination and/or Reports


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開講学期
／Semester		 2020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		 －
履修規程上の先修条件
／Prerequisites 		－
使用言語
／Language		 －

更新日／Last updated on 2020/08/18
授業の概要
／Course outline		 Combination of face-to-face classes and remote classes


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		 2020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		 －
履修規程上の先修条件
／Prerequisites 		－
使用言語
／Language		 －

更新日／Last updated on 2020/01/30
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		 To 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 and/or reports.
履修上の留意点
／Note for course registration		 線形代数１

線形代数２

応用代数

数値解析
must have been taken.
参考(授業ホームページ、図書など)
／Reference (course
website, literature, etc.)		 池辺八洲彦、池辺淑子、浅井信吉、宮崎佳典、現代線形代数 -分解定理を中心として-、共立出版、２００９
池辺八洲彦、稲垣敏之、数値解析入門、昭晃堂、１９９４


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開講学期
／Semester		 2020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		 －
履修規程上の先修条件
／Prerequisites 		－
使用言語
／Language		 －

更新日／Last updated on 2020/08/18
授業の概要
／Course outline		 Combination of face-to-face classes and remote classes

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.


科目一覧へ戻る
開講学期
／Semester		 2020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		 －
履修規程上の先修条件
／Prerequisites 		－
使用言語
／Language		 －

更新日／Last updated on 2020/05/14
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）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. 遠隔授業で実施する.
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		 2020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		 シュミット　ローター
担当教員名
／Instructor		 シュミット　ローター
推奨トラック
／Recommended track		 －
履修規程上の先修条件
／Prerequisites 		－
使用言語
／Language		 －

更新日／Last updated on 2020/02/19
授業の概要
／Course outline		 We present a self-contained theoretical framework for scaled genetic
algorithms with binary encoding which converge asymptotically to global
optima in analogy to the simulated annealing algorithm which is also
discussed. The scaled genetic algorithm employs multiple-bit mutation,
single-cut-point crossover (or other crossover) and power-law scaled
proportional fitness selection based upon an arbitrary fitness function. In
order to achieve asymptotic convergence to global optima, the mutation and
crossover rates have to be annealed to zero in proper fashion, and
power-law scaling is used with logarithmic growth in the exponent.
A detailed listing of theoretical aspects is presented including
prerequisites on inhomogeneous Markov chains. In particular, we focus on:
(i) The drive towards uniform populations in a genetic algorithm including
the undesired effect of genetic drift.
(ii) Weak and strong ergodicity of the inhomogeneous Markov chain
describing the probabilistic model for the scaled genetic algorithm.
(iii) Convergence to globally optimal solutions.
We discuss generalizations and extensions of the core framework presented
in this exposition such as other encodings, or other versions of the
mutation-crossover operator, in particular, the Vose-Liepins version of
mutation-crossover. This refers to work by L.M. Schmitt in [Theoretical
Computer Science 259 (2001), 1--61] where similar types of algorithms are
considered over an arbitrary-size alphabet, and convergence for arbitrary
fitness function under more general conditions is shown. Finally, we
present an outlook on further developments of the theory.
授業の目的と到達目標
／Objectives and attainment
goals		 Learn the mathematical theory of inhomogeneous Markov chains. Apply this to a detailed analysis of simulated annealing and genetic algorithms. Use Banach algebra techniques to obtain usable estimates for the behavior of scaled probabilistic algorithms. Show global convergence of such algorithms under certain conditions for implementation.
授業スケジュール
／Class schedule		 Introduction to Simulated Annealing and Genetic Algorithms (I).
Fundamentals of inhomogeneous Markov chains (II-III).
Simulated Annealing (IV-V).
Mutation Operator, description, estimates, weak ergodicity (VI-VII).
Crossover, description, commutation relations with mutation, estimates for mixing (VIII-IX).
Selection, description, contraction properties (X).
Convergence to uniform populations (XI).
Strong ergodicity and convergence to global maxima (XII-XIII).
Examples for convergence and non-convergence (XIII-XIV).
教科書
／Textbook(s)		 Frontiers of Evolutionary Computation
(Genetic Algorithms and Evolutionary Computation)
Springer, A. Menon
ISBN-13: 978-1402075247
成績評価の方法・基準
／Grading method/criteria		 Attendance strictly enforced. Obligation to implement some examples of the algorithms discussed above. Final exam determines the grade.
履修上の留意点
／Note for course registration		 Calculus. Linear Algebra. Introductory Probability Theory.
参考(授業ホームページ、図書など)
／Reference (course
website, literature, etc.)		 Lecture material will be handed out in class. Or email L@LMSchmitt.de.


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開講学期
／Semester		 2020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		 －
履修規程上の先修条件
／Prerequisites 		－
使用言語
／Language		 －

更新日／Last updated on 2020/08/14
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.)		 ~k-asai/classes/grds/ （授業用ディレクトリー）


科目一覧へ戻る
開講学期
／Semester		 2020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		 －
履修規程上の先修条件
／Prerequisites 		－
使用言語
／Language		 －

更新日／Last updated on 2020/09/11
授業の概要
／Course outline		 The classes will be conducted face-to-face.
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)		 Printed handouts will be distributed to students in the class.
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		 2020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		 －
履修規程上の先修条件
／Prerequisites 		－
使用言語
／Language		 －

更新日／Last updated on 2020/01/31
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		 2020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		 －
履修規程上の先修条件
／Prerequisites 		－
使用言語
／Language		 －

更新日／Last updated on 2020/08/04
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, including:

- Activity/Category theory
- Distributed cognition
- Situated action
- Participatory design
- Action research
- Emerging computing paradigms with HCC

Examples and academic papers on HCC researches (to each topics above) will be distributed to students who are enrolled in this class.

Due to the influence of COVID-19, this course will be delivered online.
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: 40%
Project: 30%
Report: 20%
Quizzes: 10%
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


科目一覧へ戻る
開講学期
／Semester		 2020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		 －
履修規程上の先修条件
／Prerequisites 		－
使用言語
／Language		 －

更新日／Last updated on 2020/01/23
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


科目一覧へ戻る
開講学期
／Semester		 2020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		 三瓶　岳昭
担当教員名
／Instructor		 三瓶　岳昭
推奨トラック
／Recommended track		 －
履修規程上の先修条件
／Prerequisites 		－
使用言語
／Language		 －

更新日／Last updated on 2020/08/31
授業の概要
／Course outline		 (This course will be conducted in the face-to-face style.)
This course provides fundamental knowledge on fluids, dynamics of fluid flows and basic methods to obtain a numerical solution of the governing equations, for students studying fluid mechanics or related disciplines such as meteorology and physical oceanography.
授業の目的と到達目標
／Objectives and attainment
goals		 In this course, students will obtain 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
3. Hydrostatic balance, Buoyancy, First law of thermodynamics, Potential temperature
4. Eulerian/Lagrangian description of fluid motion, Streamline
5. Equation of continuity, Euler's equation of motion
6. Bernoulli’s theorem and its applications
7. Vorticity, Divergence and velocity potential, two-dimensional flow fields of ideal fluid
8. Motion of viscous fluid, Navier-Stokes equation
9. Solutions of Navier-Stokes equation for simple problems, Dimensional analysis
10. Characteristics of second-order partial differential equations, Discretization of PDEs
11. Finite difference method, Accuracy and truncation error
12. Lax’s equivalence theorem, CFL condition, Analysis of numerical stability
13. Numerical schemes for advection equation, Amplification factor, Phase error
14. Numerical schemes for diffusion/Laplace equations, Application of the 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:
* Calculus (undergraduate)
* Dynamics (undergraduate)
* Numerical analysis (undergraduate)
In addition, "Numerical modeling and simulations" (graduate course) is useful to understand the subject of this course.


科目一覧へ戻る
開講学期
／Semester		 2020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		 －
履修規程上の先修条件
／Prerequisites 		－
使用言語
／Language		 －

更新日／Last updated on 2020/05/14
授業の概要
／Course outline		 Current computing environments include various kinds of endpoints like smart phones, portable PCs, 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 attacks, 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.

(note) This course is delivered via live online classes.
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

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. Attacks 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.


科目一覧へ戻る
開講学期
／Semester		 2020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		 －
履修規程上の先修条件
／Prerequisites 		－
使用言語
／Language		 －

更新日／Last updated on 2020/02/04
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 each method for signal processing with computer programming; usually it is performed by Matlab and Python. Finally, the course provides some applications, such as noise canceling, echo 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), FFT
7) FIR filters and 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] Reading materials prepared by the instructor.
[2] Statistical and Adaptive Signal Processing, Dimitris G. Manolakis, Vinay K. Ingle, and Stephen M. Kogon, Artech House, Inc., 2005, ISBN 1580536107. Many MATLAB functions are included and are available from the web page of the book.
[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		 Quiz (20 points)
Homework (28 points)
Report (52 points)
履修上の留意点
／Note for course registration		 Prerequisites:
Signal Processing and Linear System (undergraduate) or an equivalent course.
参考(授業ホームページ、図書など)
／Reference (course
website, literature, etc.)		 Monson H. Hayes, Statistical Digital Signal Processing and Modeling. John Wiley & Sons, 2009, ISBN 0471594318


科目一覧へ戻る
開講学期
／Semester		 2020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, ルバシェフスキー　イゴール, 渡部　俊朗
推奨トラック
／Recommended track		 －
履修規程上の先修条件
／Prerequisites 		－
使用言語
／Language		 －

更新日／Last updated on 2020/09/04
授業の概要
／Course outline		 The lectures will be online course via ZOOM.

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. Probability Theory:
• Discrete and continuous sample space, events
• Axioms of probability on a finite elements sample space
• Conditional probability and independence
• Bayes’ theorem and Bayes’ interpretation of probability
Topic 2: Random Variables and Probability Distributions:
• Discrete and continuous random variables
• Cumulative distribution and probability density function
• Mean value and variance
• Particular examples: uniform, binomial, geometric, Poisson, uniform, normal, standard normal, etc. distributions functions
Topic 3: Joint Probability Distributions
• Joint probability on two dimension and covariance and correlation
• Joint probability on multi-dimensional space
• Mean value and variance for multi-dimensional random values
• Moment-Generating functions and the property
Topic 4: Stochastic process and Central limit theorem
• Independent and identically distributions
• Large of number strong and weak theorem
• Central limit theorem
Topic 5: Estimates and Sampling Distributions:
• Sampling and sampling distributions
• General concepts and methods of point estimation
• Confidential intervals on the mean and the variance of a normal distribution
Topic 6: Hypothesis Testing:
• Hypothesis sampling and statistical hypothesis
• Tests on the mean and the variance of normal distribution  
• Testing for goodness of fit and equivalence testing
Topic 7: Linear Regressions and Correlations:
• Empirical models and linear regressions
• Hypothesis tests in linear regression
• Prediction of new observations
教科書
／Textbook(s)		 No Text.
成績評価の方法・基準
／Grading method/criteria		 By Reports 100％.
履修上の留意点
／Note for course registration		 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.)		 W. Feller, An Introduction to Probability Theory, Vol.1, (Wiley)
統計学入門 : 東京大学出版会.
自然科学の統計学　 : 東京大学出版会.
Douglas C. Montgomery and George C. Runger, Applied Statistics and Probability for Engineers, 6th ed. Wiley, 2014
R. Lyman Ott and Michael Longnecker, An Introduction to Statistical Methods & Data Analysis, Cengage Learning, 2016
Peter Dalgaard, Introductory Statistics with R, Springer, 2008


科目一覧へ戻る
開講学期
／Semester		 2020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, 渡邊　曜大
推奨トラック
／Recommended track		 －
履修規程上の先修条件
／Prerequisites 		－
使用言語
／Language		 －

更新日／Last updated on 2020/05/13
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.
(This is an online course.)
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. Masayuki Yamagami)
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. Yodai Watanabe)
8:  Review for the second part
9:  Quantum code (lecture)
10: Quantum code (exercise)
11: Quantum cryptography (lecture)
12: Quantum cryptography (exercise)
13: Quantum algorithm (lecture)
14: Quantum algorithm (exercise)
教科書
／Textbook(s)		 Useful books (The detail will be explained in the lecture)
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 a prerequisite.
Before registering the course, students may wish to check the following page:
http://web-int.u-aizu.ac.jp/~yodai/course/QI/welcome.html

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.


科目一覧へ戻る
開講学期
／Semester		 2020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		 －
履修規程上の先修条件
／Prerequisites 		－
使用言語
／Language		 －

更新日／Last updated on 2020/08/04
授業の概要
／Course outline		 授業は遠隔授業のみで行う。

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、すなわち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		 成績評価の基準は以下の通りである。

授業終了後に提出する最終課題 100%

期末試験は実施しない。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.


科目一覧へ戻る
開講学期
／Semester		 2020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		 －
履修規程上の先修条件
／Prerequisites 		－
使用言語
／Language		 －

更新日／Last updated on 2020/08/04
授業の概要
／Course outline		 Most (if not all) engineering problems can be formulated as optimization problems. 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, and 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		 The students are encouraged to study "artificial intelligence" (under graduate course) first.


All lectures will be conducted online. We will take the record of each class and post it to the Moodle system. Please watch the video if you cannot follow the class well.
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


科目一覧へ戻る
開講学期
／Semester		 2020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, 渡邊　曜大
推奨トラック
／Recommended track		 －
履修規程上の先修条件
／Prerequisites 		－
使用言語
／Language		 －

更新日／Last updated on 2020/08/14
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.)		 ~k-asai/classes/graph/ (Directory for the class)


科目一覧へ戻る
開講学期
／Semester		 2020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, ルバシェフスキー　イゴール
推奨トラック
／Recommended track		 －
履修規程上の先修条件
／Prerequisites 		－
使用言語
／Language		 －

更新日／Last updated on 2020/09/04
授業の概要
／Course outline		 In 2020 Q4, the lecture and exercise will be mainly given as offline. Depending on the situation, the plan might be changed.

This course mainly introduces

1. Ordinary and partial differential equations appear in science or engineering
2. Schemes to discretize the differential equations , and
3. Computational techniques to get the 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 Python or similar languages. In particular, a few topic will be selection from the following problems:
a. Stability and accuracy of numerical simulation
b. Elliptic Equations
c. Chaos dynamics
d. Systems with delay
c. Suppressing numerical instabilities in solving partial differential equations,
e. Stochastic differential equations

This course starts with theory and mathematics of differential equations followed by hands-on style exercises as well as computer-related exercises under Python or R-language on numerical techniques to solve various differential equations.
授業の目的と到達目標
／Objectives and attainment
goals		 A main goal of this course is to introduce basic theory of differential equations and a 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 and R-language for obtaining preliminary results with their 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 & N.Asai)
week 3 Introduction to Partial Differential Equations (N.Nakasato & N.Asai)
week 4 Topics in Partial Differential Equations (1) (N.Nakasato & N.Asai)
week 5 Topics in Partial Differential Equations (2) (N.Nakasato & I. Lubashevsky)
week 6 Practical Applications of Numerical Modeling and Simulations (N.Nakasato,  I. Lubashevsky & N.Asai)
week 7 Parallel Computing (N.Nakasato)
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 (50 points)
Report (50 points)
履修上の留意点
／Note for course registration		 Numerical Analysis (undergraduate course) and related courses.


科目一覧へ戻る
開講学期
／Semester		 2020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　有隆
担当教員名
／Instructor		 渡部　有隆, 西舘　陽平
推奨トラック
／Recommended track		 －
履修規程上の先修条件
／Prerequisites 		－
使用言語
／Language		 －

更新日／Last updated on 2020/05/19
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 string, networks, 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. String Matching. Suffix Arrays and Trees, Rolling Hash, Trie, etc.
6. Network Flow. 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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