2017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)

2018/01/30  現在

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開講学期
／Semester		 2017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, スー　チュンホワ
推奨トラック
／Recommended track		 －
履修規程上の先修条件
／Prerequisites 		－

更新日／Last updated on 2017/01/30
授業の概要
／Course outline		 Current computing environments include various kinds of endpoints like smart phones, portable PCs, desktop PCs, server and cluster computers, and virtual machines on cloud computing platforms. To show the capabilities and performance of them and prevent accidents and attacks, 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		 L1, 2. Fundamentals
- Goal of information security
- Risk, threat, vulnerability, and control
- Confidentiality, integrity, availability (C-I-A) triad
- Attack paradigm and protection paradigm

L3, 4. Vulnerability
- Classes of vulnerabilities
- Trends of vulnerability
- Vulnerability management
- Open standards for vulnerability management

L5, 6. 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)

L7, 8. Relation among security notions
- Equivalence between SS and IND
- NM implies IND, IND-CCA2 implies NM-CCA2

L9, 10. Web application security
- Most critical risks
- Critical controls for defense
- Assessment tools

L11, 12. Attacks and controls 1: DoS and DDoS attacks
- Nature and types
- Methods
- Countermeasures

L13, 14. Attacks and controls 2: Password cracking
- Factors for authentication
- Types of password cracking
- Password attack strategies

L15. Final review and concluding remarks
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		 Assignments
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.)		 Cryptography part: http://web-int.u-aizu.ac.jp/~yodai/course/SEC/welcome.html


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開講学期
／Semester		 2017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		 丁　数学
担当教員名
／Instructor		 丁　数学, - -
推奨トラック
／Recommended track		 －
履修規程上の先修条件
／Prerequisites 		－

更新日／Last updated on 2017/01/16
授業の概要
／Course outline		 Statistical Signal Processing is very important in various
applications including signal detection, noise cancellation,
synchronization, communications, and instrumentations.
Therefore, this course is provided as a core course for the
graduate school. This course gives a unified introduction to
the theory, implementation, and applications of statistical
signal processing methods. This course considers stochastic
signals/systems, rather than deterministic signals/systems,
as in the undergraduate course of “Digital signal processing”.
Focus is on 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 course
for graduate students studying in all the fields of information
system. It presents a methodology for signal processing with
statistical features of the signal, which is the essence of an
information system. Furthermore, the course presents
implementation of each method for statistical signal
processing with a computer; usually it is performed by Matlab.
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		 Chapter 1 Introduction
Chapter 2 Fundamentals of discrete-time signal processing
Chapter 3 Random variables, sequences, and stochastic
    process
Chapter 4 Linear nonparametric signal models
Chapter 5 Non-parametric power spectrum estimation
Chapter 6 Optimum linear filters, Wiener filter
Chapter 7 Algorithms and structures for optimum linear
    filters
Chapter 8 Kalman filter
Chapter 9 Least-squares filtering and prediction
Chapter 10 Adaptive filters
教科書
／Textbook(s)		 [1] Reading materials prepared by the instructor.
[2] Dimitris G. Manolakis, Vinay K. Ingle, and Stephen
M. Kogon, Statistical and Adaptive Signal Processing,
Artech House, Inc., 2005, ISBN 1580536107.
Many MATLAB functions are included and are available
from the web page of the book.
成績評価の方法・基準
／Grading method/criteria		 Attendance (20 points)
Homework (30)
Report (50 points)
履修上の留意点
／Note for course registration		 Digital signal processing (undergraduate)
Linear Systems (undergraduate)
参考(授業ホームページ、図書など)
／Reference (course
website, literature, etc.)		 Alexander D. Poularikas, and Zayed M. Ramadan,
Adaptive Filtering Primer with Matlab, CRC Press
(Feb. 2006).


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開講学期
／Semester		 2017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		 渡部　俊朗
担当教員名
／Instructor		 渡部　俊朗, 土屋　貴裕
推奨トラック
／Recommended track		 －
履修規程上の先修条件
／Prerequisites 		－

更新日／Last updated on 2017/01/20
授業の概要
／Course outline		 This course provides advanced contents of applied statistics based on an undergraduate course " Probability and 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 have introductory knowledge on stochastic processes.
授業スケジュール
／Class schedule		 Review of Probability(確率の復習 ) I,II,III.

Sample distributions(標本分布) I,II.

Estimation and Test of mean and variance (平均と分散の推定と検定) I,II.

Test of goodness of fit(適合度検定) I,II.

Linear regression(線形回帰) I,II.

Analysis of variance (分散分析) I,II.

Stochastic processes(確率過程) I,II
教科書
／Textbook(s)		 No Text.
成績評価の方法・基準
／Grading method/criteria		 By Reports.
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.)		 Feller, W. : An Introduction to Probability Theory, Vol.1, (Wiley)
白旗慎吾 : 統計解析入門, 共立出版.
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.


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開講学期
／Semester		 2017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		 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 		－

更新日／Last updated on 2017/01/26
授業の概要
／Course outline		 このコースでは量子情報と量子計算および量子暗号の基本を学ぶ。
授業の目的と到達目標
／Objectives and attainment
goals		 このコースをとることにより、学生は
1) 量子情報に関する量子力学
2) 量子通信の基礎知識
3) 量子計算のアルゴリズム
4) 量子暗号
等が学べる。
授業スケジュール
／Class schedule		 1. 量子力学の数学的基礎
2. 量子力学の基礎概念 Basic Concepts of Quantum Mechanics
3. EPR対と観測問題 EPR pair and measurement
4. 量子ゲート Quantum Gates
5. 情報・通信の理論 Theory of Information and Communication
6. 量子計算 Quantum Algebra
7. ショアの因数分解のアルゴリズム Shor's Algorithm
8. 量子誤り訂正 Quantum Error Correction
9. 量子暗号 Quantum Cryptography
10. 量子アルゴリズム Quantum Algorithm
教科書
／Textbook(s)		 参考書（講義の第1回目に説明がある）
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)
2-j. 量子情報理論(佐川弘幸 / 吉田宣章, 丸善出版, 日本語版)
2-e. Fundamentals of Quantum Information(H. Sagawa and N. Yoshida, World Scientific, in English)
成績評価の方法・基準
／Grading method/criteria		 出席 およびレポート
履修上の留意点
／Note for course registration		 量子力学と線形代数の基礎知識があることが望ましい。
ただし、量子情報理論を学ぶ積極的な意志があれば履修可能。


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開講学期
／Semester		 2017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 		－

更新日／Last updated on 2017/01/30
授業の概要
／Course outline		 Computation is one of the most important concepts in computer science, and indicates the limit of the power of computers, which should be familiar to all people working in computer science or engineering field.
By this notion, the problems are classified into two classes, that is, the class consisting computable or solvable problems and one consisting incomputable or unsolvable problems.
授業の目的と到達目標
／Objectives and attainment
goals		 Students can be familiar to the notion of computability defined by several methods, such as Turing machines, register machines, recursive functions. Furthermore, they can understand the limit of the computers, that is, that there are problems which cannot be solved by any computer.
授業スケジュール
／Class schedule		 Meeting 1.
1. Introduction

Meetings 2 -- 7.
2. Computation Models
2.1 Turing Machine Model
2.2 Random Access Machine(RAM) Model
2.3 Recursive Function Model
2.4 While Program Model

Meetings 8 -- 10.
3. Church-Turing Thesis
3.1 The equivalence in the computation power among models
3.2 Church-Turing Thesis

Meetings 11 -- 13.
4. Universal Program
4.1 Coding of Programs
4.2 Construction of Universal Program

Meetings 14 -- 16.
5. Unsolvable Problems
5.1 Halting Problem
5.2 Reducibility
5.3 Post's Corresponding Problem
6. Further Topics

The topis above may be changed according to the progress of the course.
教科書
／Textbook(s)		 We do not specify textbooks but introduce books related to the topics in the course.
The lectures proceeds according to the handouts distributed during the class.
成績評価の方法・基準
／Grading method/criteria		 Small quizzes will be given. A report on the topics concerning this course will be required as the final examination.
履修上の留意点
／Note for course registration		 Students enrolling this course had better to be familiar to the fundamental concepts studied in F3 Discrete Systems, F1 Algorithms and Data Structure, F8 Automata and languages, and M9 Mathematical Logic in the undergraduate program, although they are not the prerequisite courses.
参考(授業ホームページ、図書など)
／Reference (course
website, literature, etc.)		 There are so many books on this fields. Some of them will be introduced in the class.


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開講学期
／Semester		 2017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 		－

更新日／Last updated on 2017/01/24
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. Metaheuristics are multi-level heuristics that can control the whole process of search, so that global optimal solutions can be obtained systematically and efficiently. Although metaheuristics cannot always guarantee to obtain the true global optimal solution, they can provide very good results for many practical problems. Usually, metaheuristics can enhance the computing power of a computer system greatly without increasing the hardware cost.

So far many metaheuristics have been proposed in the literature. In this course, we classify metaheuristics 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. Mathematic 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, we should be able to

(1) Understand the basic ideas of each metaheuristics algorithm;
(2) know how to use metaheuristics 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 - I
    - Meme, memotype, memeplex, and memetic evolution.

(12) Memetic algorithms - II
    - Combination of memetic algorithm and genetic algorithm.

(13) Swarm Intelligence
    - Ant colony optimization and particle swarm optimization

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

(15) Presentation of team work II.
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		 Attendance and quiz: 10 points.
Team work: 50 (25 x 2) points.
Presentation: 40 (20 x 2) points.
履修上の留意点
／Note for course registration		 * Artificial intelligence
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		 2017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 		－

更新日／Last updated on 2017/01/19
授業の概要
／Course outline		 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 focus to carefully selected important topics, and advance our knowledge in that area. For example, we focus to vertex/edge connectivity, and introduce Menger's theorem and Mader's theorem; also focus to 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, 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
4. Connectivity
5. Distance and diameter; Coloring
6. Special graphs, matrices
7. Eulerian/Hamiltonian multigraphs
8. Connectivity (revisited)
9. Menger's theorem, Mader's theorem
10. Planarity (optional)
11. Trees, Spanning trees and Kirchhoff's theorem
12. Deletion-contraction method
13. Prufer's bijective proof of Cayley's formula
14. Minimum spanning trees
15. Decomposition of graphs
16. 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		 By presentation and reports.
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)


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開講学期
／Semester		 2017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, イゴール　ルバシェフスキー
推奨トラック
／Recommended track		 －
履修規程上の先修条件
／Prerequisites 		－

更新日／Last updated on 2017/01/30
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 , and
3. Computational techniques to get the numerical solutions.
4. Use of numerical libraries for solving differential equations and visualizing the results of simulation; main attention is focused on the use of Python and R-language. Using Python and R-language the following problems are considered, in particular
a. Stability and accuracy of numerical simulation
b. Nonlinear oscillations
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 and 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. Julia, which is a high-level, high-performance dynamic programming language for technical computing (http://julialang.org/).
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&N.Asai)
week 2 Advanced Theory of Ordinary Differential Equations (N.Nakasato&N.Asai)
week 3 Topics and exercise in Ordinary Differential Equations (N.Nakasato&I. Lubashevsky)
week 4 Introduction to Partial Differential Equations (N.Nakasato&N.Asai)
week 5 Advanced Theory of Partial Differential Equations (N.Nakasato&N.Asai)
week 6 Topics in Partial Differential Equations (1) (N.Nakasato&N.Asai)
week 7 Topics in Partial Differential Equations (2) (N.Nakasato&I. Lubashevsky)
week 8 Exercise of Partial Differential Equations  (N.Nakasato&I. Lubashevsky)
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.
参考(授業ホームページ、図書など)
／Reference (course
website, literature, etc.)		 http://galaxy.u-aizu.ac.jp/note/wiki/NMS2016


科目一覧へ戻る
開講学期
／Semester		 2017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 		－

更新日／Last updated on 2017/01/31
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
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
6. Introduction to many-core architectures
7. Introduction to CUDA and OpenCL languages
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		 Labs = 20%, Assignments = 10%, Project = 60%, Participation = 10%
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://www.oscer.ou.edu/education.php


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開講学期
／Semester		 2017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		 三瓶　岳昭
担当教員名
／Instructor		 三瓶　岳昭, ハミード　サジ
推奨トラック
／Recommended track		 －
履修規程上の先修条件
／Prerequisites 		－

更新日／Last updated on 2017/01/13
授業の概要
／Course outline		 This course is an introductory course and provides fundamental knowledge on fluids, dynamics of fluid flows and basic methods for obtaining a numerical solution of the governing equations.
授業の目的と到達目標
／Objectives and attainment
goals		 In this course, the students will obtain basic understanding of fluid properties and principles of fluid dynamics, and learn how to solve some simple problems of fluid dynamics through numerical integration of the governing equations.
授業スケジュール
／Class schedule		 1. Introduction, Properties of fluids, Equation of state, Viscosity, Laminar and turbulence
2. Fluid statics, Hydrostatic balance, First law of thermodynamics
3. Eulerian/Lagrangian description of fluid motion, Streamline, Equation of continuity
4. Euler’s equation of motion, Bernoulli’s theorem, Vorticity and stream function, Divergence and velocity potential
5. Navier-Stokes equation and its application to some simple problems, Dimensional analysis, Reynolds number, Similarity of flow
6. Characteristics of second-order partial differential equations, Wave/advection equation, Diffusion equation, Laplace/Poisson equation
7. Discretization of PDEs, Finite difference method, Lax’s equivalence theorem, CFL condition, Analysis of numerical stability
8. Various numerical schemes for integration, 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 papers, Attendance.
履修上の留意点
／Note for course registration		 Prerequisites:
• Calculus (undergraduate)
• Linear algebra (undergraduate)
• Dynamics (undergraduate)
• Numerical analysis (undergraduate)
In addition, “Numerical modeling and simulations” (graduate course) is desirable.


科目一覧へ戻る
開講学期
／Semester		 2017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 		－

更新日／Last updated on 2017/01/25
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)  Team project ? VI: Pattern recognition based on neural network trees.

15)  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		 Attendance: 15
Projects: 60 (10 x 6)
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)  Jacek M. Zurada, Introduction to Artificial Neural Systems, PWS Publishing Company, 1995.

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

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

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

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

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


科目一覧へ戻る
開講学期
／Semester		 2017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		 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 		－

更新日／Last updated on 2017/01/25
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. Rock-Paper-Scissors Problem by Cooperative Evolutionary Learning
    Rock-paper-scissors problem is a familiar game that has been playing
    world wide. It may seem like a trival game. However, it actually
    involves the hard computational problem of finding an optimal
    strategy. This lecture will introduce how computer program could
    automatically evolve such an optimal strategy based on
    cooperative evolutionary learning.

5. 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.

6. Evolvable Systems
    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.
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 select a project among the provided
topics, and give a presentation at the end of the course.
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.


科目一覧へ戻る
開講学期
／Semester		 2017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		 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 		－

更新日／Last updated on 2017/01/22
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


科目一覧へ戻る
開講学期
／Semester		 2017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		 モハメド　ハマダ
担当教員名
／Instructor		 モハメド　ハマダ
推奨トラック
／Recommended track		 －
履修規程上の先修条件
／Prerequisites 		－

更新日／Last updated on 2017/01/23
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. General Introduction
2. Computational models
3. Abstract reduction systems
4. Universal Algebra
5. Term rewriting systems
6. Unification
7. Midterm Report
8. Termination
9. Confluence
10. Completion
11. Applications
12. General review
13. Final Report and 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		 In general the evaluation procedure will be
carried out as follows.
1. Students are expected to give some presentations/seminars.
2. Students are expected to submit up to two reports: one at midterm and one
at the final of the course.
3. Students are expected to write some programs (they can use any
programming language they like).
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.


科目一覧へ戻る
開講学期
／Semester		 2017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 		－

更新日／Last updated on 2017/01/29
授業の概要
／Course outline		 This is a topic course: several topics of matrix based numerical computation will be selected, especially properties of matrices from the view point of decomposition theorems are discussed.
授業の目的と到達目標
／Objectives and attainment
goals		 Give a summary of properties of matrices on matrix computations.
授業スケジュール
／Class schedule		 1. Matrix operations

2. Matrix operations

3. Vector space and linear transformation

4. Vector space and linear transformation

5. Matrix Decompositions

6. Equivalent Decomposition

7. LDU Decomposition

8. Determinant and Inner Product

9. QR Decomposition

10. QR Decomposition

11. Shur Decomposition

12. Shut Decomposition

13. Jordan Decomposition

14. Jordan Decomposition

15. Singular value Decomposition
16. Singular value Decomposition and CS Decomposition
教科書
／Textbook(s)		 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
成績評価の方法・基準
／Grading method/criteria
Participation, Quizzes and/or reports are totally considered.
履修上の留意点
／Note for course registration		 下記科目を履修していること
線形代数１

線形代数２

数値解析


科目一覧へ戻る
開講学期
／Semester		 2017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		 モハメド　ハマダ
担当教員名
／Instructor		 モハメド　ハマダ
推奨トラック
／Recommended track		 －
履修規程上の先修条件
／Prerequisites 		－

更新日／Last updated on 2017/01/23
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

2. Review of the theory of automata and languages
Methods to describe infinite sets
Grammars as generating systems of infinite sets
Automata as recognizing systems of infinite sets
Chomsky hierarchy of language classes
Relation among grammars and automata

3. Topics on the theory of automata and languages
Subclasses languages defined by automata with restrictions
Subclasses languages defined by grammars with restrictions
Operations on languages
Homomorphic characterization of language classes

4. Applications of the theory of automata and languages
Graph languages
Application to the cryptography
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		 Small quizzes will be given. A report on the topics concerning this course will be required as the final examination.


履修上の留意点
／Note for course registration		 Automata and Languages(F8) given in the undergraduate program is designated as
the only prerequisite course officially.
Moreover students who will register this course are expected to be familiar
to the fundamental notions of Discrete Systems(F3) and Algorithms and Data Structures(F1).

The ability of logical thinking is expected.

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		 2017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 		－

更新日／Last updated on 2017/01/23
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.
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		 学部で習った数学の復習
関数空間論入門
関数解析入門
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
履修上の留意点
／Note for course registration		 フーリエ解析, 複素関数論


科目一覧へ戻る
開講学期
／Semester		 2017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		 ローター　シュミット
担当教員名
／Instructor		 ローター　シュミット
推奨トラック
／Recommended track		 －
履修規程上の先修条件
／Prerequisites 		－

更新日／Last updated on 2017/01/31
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 (XIV-XV).
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.


科目一覧へ戻る
開講学期
／Semester		 2017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 		－

更新日／Last updated on 2017/01/19
授業の概要
／Course outline		 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. 有限体．
6. 有限体の構造．
7. 原始元．
8. フローベニウスサイクル．
9. 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		 プレゼンテーション，レポート．
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		 2017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 		－

更新日／Last updated on 2017/01/17
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)		 Printed handouts will be distributed to students in the class.
成績評価の方法・基準
／Grading method/criteria		 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


科目一覧へ戻る
開講学期
／Semester		 2017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 		－

更新日／Last updated on 2017/01/30
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 and Examination.


科目一覧へ戻る
開講学期
／Semester		 2017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		 イゴール　ルバシェフスキー
担当教員名
／Instructor		 イゴール　ルバシェフスキー
推奨トラック
／Recommended track		 －
履修規程上の先修条件
／Prerequisites 		－

更新日／Last updated on 2017/01/30
授業の概要
／Course outline		 The goal of the course is to demonstrate the basic approaches to the analysis of stochastic (random) processes and the numerical methods of their simulation. An essential pedagogical point of the course is that the main theoretical constructions are illustrated by “on-line” computer simulations and visualization with Python. The use of Python opens for the students an efficient way to the available numerical libraries (also C & C++ libraries) for simulation and visualization of scientific data.
授業の目的と到達目標
／Objectives and attainment
goals		 Stochastic behavior is exhibited by a wide variety of systems different in nature and its understanding as well as a certain skill in computer simulation of various linear and nonlinear stochastic processes is essential in many research activities, engineering applications, and statistic analysis.
It is expected that in the final phase of the course the students will acquire knowledge about
- the main models of stochastic phenomena observed in systems of various nature,
- the basic algorithms used in simulating stochastic processes,
and gain some skill in
- programming stochastic processes,
- using Python for scientific simulation visualization.
授業スケジュール
／Class schedule		 Main Blocks:
1. Getting started with Python for science, scientific Python building blocks, python IDEs, debugging tools. Basic elements of programming: data structures, control statements, functions, classes, popular modules. Scientific data visualization with Matplotlib library and  Mayavi library for plotting 3D-data. Basic elements of simulation with NumPy library.

2. SciPy and numerical methods: Linear algebra algorithms, interpolation and curve fitting. SciPy and numerical methods: Numerical Differentiation and Integration, Ordinary Differential Equations. Random and pseudo-random numbers, Random number generators, Uniform deviates, Algorithms for generating deviates from other distributions.

3. The basic notions of the probability theory. Unpredictability, stochasticity, chaos. Simulation and visualization of stochastic and chaotic trajectories of particle motion (simple pedagogical examples by 'on-line' computing). Random variables, Probability distribution, Generating function, Markov process, Markovian Brownian motion, the Central Limit Theorem (qualitative derivation),

4. The Chapman-Kolmogorov equation as a classification of random trajectories, The forward Fokker-Planck equation, Boundary conditions for the Fokker-Planck equation and their meaning, Numerical algorithms of solving the Fokker-Planck equations, illustrative computer examples. The backward Fokker-Planck equation, First passage time problem, Mean exit time, Distribution of exit points, Reversible and non-reversible random walks, Escaping from potential well and metastable states, computer illustration of escaping dynamics. Extreme events, Extreme value theory and the first passage time problem, Characteristic examples.

5. The Langevin approach and its relation to the Fokker-Planck equation, the Langevin equation with additive noise, characteristic examples, methods of its numerical solution, Stochastic Runge-Kutta algorithms. The Langevin equation of multiplicative noise, the Ito, Stratonovich, Hanngi-Klimontovich stochastic differential equations, algorithms of their numerical solution, noise-induced phase transitions, computer illustration.

6. Master equation (forward and backward ones), Detailed balance, Ergodicity, Simulation of stochastic many-particle ensembles: Monte-Carlo simulation of equilibrium systems, Monte-Carlo simulation of non-equilibrium systems, Calculations based on Monte-Carlo simulation.
教科書
／Textbook(s)		 Langtangen, Hans Petter. A Primer on Scientific Programming with Python, Springer, 2012
Langtangen, Hans Petter. Python Scripting for Computational Science, Springer, 2008
Kiusalaas, Jaan. Numerical Methods in Engineering with Python, Cambridge University Press, 2013.
Alexandre Devert, Matplotlib Plotting Cookbook, Packt Publishing, 2014.
Sandro Tosi. Matplotlib for Python Developers, Packt Publishing, 2009.
Eli Bressert. SciPy and NumPy, O’Reilly, 2012
Ivan Idris. NumPy Beginner's Guide, Packt Publishing, 2013
Ronan Lamy. Instant SymPy Starter, Packt Publishing, 2013.
N.G. van Kampen, Stochastic processes in physics and chemistry (Elsevier, Amsterdam, 2007) 3rd ed.
C.W. Gardiner, Handbook of stochastic methods (Springer-Verlag, Berlin, 2004), 3rd ed.
W. H. Press , S.A. Teukolsky , W.T. Vetterling , B.P. Flannery, Numerical recipes, (Cambridge University Press , Cambridge, 2007) 3rd ed.
W. Horsthemke and R. Lefever, Noise-Induced Transitions (Springer,  Berlin, 1984).
成績評価の方法・基準
／Grading method/criteria		 Homework Assignments: 50%; Final Examination 50%
履修上の留意点
／Note for course registration		 Calculus,
Complex variables,
Probability theory.


科目一覧へ戻る
開講学期
／Semester		 2017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, 渡部　俊朗
推奨トラック
／Recommended track		 －
履修規程上の先修条件
／Prerequisites 		－

更新日／Last updated on 2017/01/23
授業の概要
／Course outline		 ``Considering particles that move randomly depend on geographical character of the present position, what phenomena should be happen?’’
As it may be seemed that this question is far from mathematical problems, but there is a strong connection between the problem and mathematics.
In fact, Kolmogorov formulated this problem via constructing theory for stochastic process and showed these phenomena could be characterized by partial differential equations of second.
Then, the problem was explored in more detail by K. Ito in view of “sample paths”.
He defined ``stochastic integrals" to justify the definition of integrals driven by Wiener process which has infinite variation and he proved the Ito formula that allows us to analysis stochastic processes in probabilistic systems.
In this lecture, we focus on the discrete stochastic processes
of optimal stochastic problems to acquire the mathematical principles.
授業の目的と到達目標
／Objectives and attainment
goals		 To be able to understand the rich mathematical structure of a random phenomena and to develop an ability to consider from the approach taken by stochastic process.
授業スケジュール
／Class schedule		 1. Introduction
2. Algebra and measurable space
3. Probability measure
4. Random value
5. Filtration
6. Stopping time
7. Measurability
8. Expectation
9. An easy strong law
10. Quiz
11. Conditional expectation
12. Conditional Probability measure and its application
13. A mathematical formulation on random systems  
14. A solution and its generalization
15. Final  
教科書
／Textbook(s)		 Probability with Martingales (Cambridge Mathematical Textbooks)   1991/2/14
David Williams
成績評価の方法・基準
／Grading method/criteria		 The following activities and percentage determine your grades:
Quiz 40%
Final 40%
Homework 20%
履修上の留意点
／Note for course registration		 M-3 微積分I又はM-4 微積分 II, M-1 線形代数 I又はM-2 線形代数 II, フーリエ解析.


科目一覧へ戻る
開講学期
／Semester		 2017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 		－

更新日／Last updated on 2017/01/31
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. Tentative syllabus is designed:

Lesson 1. Human-centered Computing: At a glance
A brief summary of Human-centered Computing will be given in the first class. Students are expected to have a comprehensive view of this area and know what are going to be taught in the following lectures.

Lesson 2. Sociology of Science and Technology - 1
Essentials of sociology are taught at the beginning, and their derive issues in the area of computer science and technology will be gone through. At this lesson, we concentrate on the computational sociology such as sociological theory and method, applied sociology, social construction of technology, and human ecology.

Lesson 3. Sociology of Science and Technology - 2
With the basics, this lesson goes further to introduce present situations and related researches, providing a chance for students to think and build their own understanding of this topic.

Lesson 4. Epistemology
This lesson introduces the basics of epistemology and its usage in implementing the human-centered scenarios. Sub-topics including fundamentals of knowledge, knowledge discovery and management, and tendency of epistemology, will be concentrated.

Lesson 5. Activity/Category Theory
Activity theory and category theory are approaches to understanding human behavior by examining social contexts (e.g., behaviors, motivations to specific purposes). This lesson introduces the basics of activity/category theory, and their usage and differences in system development are then discussed.

Lesson 6. Discussions & Presentations
New information and results of survey will be presented. After the presentation, students will be divided into groups (depending on number of students) for preparing the projects due at end of this semester.

Lesson 7. Distributed Cognition ? 1
In the following two weeks, an essential to human-centered system design is introduced. The theoretical part, such as cognitive theory of learning, cognition science, socially cognition, etc., and the methods to them will be given. Students are requested to bring his/her previous works (or projects) to the class for discussions.

Lesson 8. Distributed Cognition ? 2
Following the previous lesson, this lesson then gives real-world practices on the introduced theories and methods. Students are expected to cultivate the ability of system design.

Lesson 9. Action Research
This lesson pays more attention on the methods to implement the theory of action research than the theory itself. The comparison is also given between action research and software engineering. Students are expected to understand the similarity and difference among them.

Lesson 10. Participatory Design
An important factor, i.e., user, in software (or system) development is introduced. Approaches to help ensure the software design meets the needs and is usable are discussed. Some terms in software design such as sustainability, flexibility, extensibility, and etc. will be emphasized.

Lesson 11. User-centered Design
This lesson begins by developing a strong theoretical foundation in user-centered design, and various fields and testing methods including usability testing, contextual inquiry, and design-focused ethnographic methods are then explored.

Lesson 12. Assistive Technology
History and current state of assistive technology and the challenges that remain today are introduced. The specific concepts explored in this lesson include contextual inquiry, user analysis, and evaluation metrics.

Lesson 13. HCC in Emerging Computing Paradigms
This lesson talks about the HCC in pervasive computing, social computing and cloud computing.

Lesson 14. Discussions & Presentations
Assigned research papers will be presented and discussed. Following the presentation, few emerging topics will be given for free discussions.

Lesson 15. Project Demonstration
Each group will demonstrate the projects with details on motivation (and background), introduction to system design, and evaluation on HCC factors in the design.
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: 50%
Project: 30%
Attendance: 20%
履修上の留意点
／Note for course registration		 N/A
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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