AY 2019 Graduate School Course Catalog

Field of Study CS: Computer Science

2019/07/22

Back
開講学期
/Semester
2019年度/Academic Year  2学期 /Second Quarter
対象学年
/Course for;
1st year , 2nd year
単位数
/Credits
2.0
責任者
/Coordinator
Akihito Nakamura
担当教員名
/Instructor
Akihito Nakamura, Yodai Watanabe, Chunhua Su
推奨トラック
/Recommended track
履修規程上の先修条件
/Prerequisites

更新日/Last updated on 2019/02/14
授業の概要
/Course outline
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, information security and management technologies for administrative work are must-have features.
        This course introduces concepts and mechanisms of computer and network security and management. We will also review several state-of-the-art real-world technologies and tools.
授業の目的と到達目標
/Objectives and attainment
goals
- Acquisition of fundamental knowledge of theoretical and practical security.
- Acquisition of basic skill and knowledge to administrate ICT systems security.
授業スケジュール
/Class schedule
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. Cryptography for IoT Security and Privacy
- Cryptography for IoT
- Side channel attacks
- Authentication by IoT

L11, 12. System security
- OS security
- Password and access control
- FinTech security

L13, 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) 100%

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.


Back
開講学期
/Semester
2019年度/Academic Year  3学期 /Third Quarter
対象学年
/Course for;
1st year , 2nd year
単位数
/Credits
2.0
責任者
/Coordinator
Takahiro Tsuchiya
担当教員名
/Instructor
Takahiro Tsuchiya, Igor Lubashevskiy, Toshiro Watanabe
推奨トラック
/Recommended track
履修規程上の先修条件
/Prerequisites

更新日/Last updated on 2019/02/22
授業の概要
/Course outline
This course provides advanced contents of applied statistics. Most important statistical methods are explained with many examples of data. At the same time, their mathematical foundations are given.
授業の目的と到達目標
/Objectives and attainment
goals
Students can understand basic applied statistics such as estimation, test, regression, and analysis of variance by using Gaussian, t, F, and chi-square distributions. Moreover, they can learn knowledge on stochastic processes.
授業スケジュール
/Class schedule
The course contains following topics with 3-4 hours for each including lecture and practical exercises.

Topic 1. 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


Back
開講学期
/Semester
2019年度/Academic Year  1学期 /First Quarter
対象学年
/Course for;
1st year , 2nd year
単位数
/Credits
2.0
責任者
/Coordinator
Masayuki Yamagami
担当教員名
/Instructor
Masayuki Yamagami, Yodai Watanabe
推奨トラック
/Recommended track
履修規程上の先修条件
/Prerequisites

更新日/Last updated on 2019/02/11
授業の概要
/Course outline
This course provides basic knowledge of quantum information and quantum computations for graduate students who want to learn modern information and computational models.
授業の目的と到達目標
/Objectives and attainment
goals
At the end of the course, students can acquire
1) Basin knowledge of quantum mechanics for quantum information theory
2) Basic knowledge of quantum transportation
3) Algorithms of quantum computers
4) 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. Interface(インターフェース) 2019年 03 月号 (CQ出版, in Japanese)
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
It is recommended to have basic knowledge of quantum mechanics and linear algebra.
However, the course will accept students who do not have any knowledge of quantum mechanics but have strong desire to learn new knowledge


Back
開講学期
/Semester
2019年度/Academic Year  1学期 /First Quarter
対象学年
/Course for;
1st year , 2nd year
単位数
/Credits
2.0
責任者
/Coordinator
Taro Suzuki
担当教員名
/Instructor
Taro Suzuki, Yodai Watanabe
推奨トラック
/Recommended track
履修規程上の先修条件
/Prerequisites

更新日/Last updated on 2019/02/14
授業の概要
/Course outline
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.
This course provides the notion of computation and computability through several computation models as the rigorous concepts of computation and their equivalence, and the existence of non-computable problems and some concrete examples of non-computable 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 computation models, such as Turing machines, register machines, recursive functions. Furthermore, they can understand the limit of the computers, that is, that there are problems which are not solved by any computer.
授業スケジュール
/Class schedule
Class 1.
1. Introduction to Computablity and Computation Models

Classes 2,3,4,5,6,7.
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

Classes 8,9,10.
3. Church-Turing Thesis
3.1 The equivalence in the computation power among models
3.2 Church-Turing Thesis
3.3 Computation on data types other than natural numbers.

Classes 11,12.
4. Universal Programs
4.1 Coding of Programs
4.2 Construction of Universal Program

Classes 13,14.
5. Unsolvable Problems
5.1 Halting Problem
5.2 Reducibility

The correspondence between classes and topics described above may be changed according to the progress of the course.
教科書
/Textbook(s)
We do not specify textbooks. The lectures proceed according to the handouts distributed during the classes.
成績評価の方法・基準
/Grading method/criteria
We don't have the final exam.The evaluations will be done by the final assignment  distributed to the students by e-mail soon after the last class,. The students should submit answers to the final assignment by the specified date. Some exercises  will be given during almost every class, too. Though they are not included in the evaluation, the students should solve them during classes; otherwise it would be difficult to answer the questions in the final assignment.
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 mandatory.
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.

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.


Back
開講学期
/Semester
2019年度/Academic Year  3学期 /Third Quarter
対象学年
/Course for;
1st year , 2nd year
単位数
/Credits
2.0
責任者
/Coordinator
Qiangfu Zhao
担当教員名
/Instructor
Qiangfu Zhao, Yong Liu
推奨トラック
/Recommended track
履修規程上の先修条件
/Prerequisites

更新日/Last updated on 2019/01/28
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.
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.
履修上の留意点
/Note for course registration
The students are encouraged to study "artificial intelligence" (under graduate course) first.
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


Back
開講学期
/Semester
2019年度/Academic Year  2学期 /Second Quarter
対象学年
/Course for;
1st year , 2nd year
単位数
/Credits
2.0
責任者
/Coordinator
Kazuto Asai
担当教員名
/Instructor
Kazuto Asai, Yodai Watanabe
推奨トラック
/Recommended track
履修規程上の先修条件
/Prerequisites

更新日/Last updated on 2019/02/07
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 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)


Back
開講学期
/Semester
2019年度/Academic Year  4学期 /Fourth Quarter
対象学年
/Course for;
1st year , 2nd year
単位数
/Credits
2.0
責任者
/Coordinator
Naohito Nakasato
担当教員名
/Instructor
Naohito Nakasato, Nobuyoshi Asai, Igor Lubashevskiy
推奨トラック
/Recommended track
履修規程上の先修条件
/Prerequisites

更新日/Last updated on 2019/02/15
授業の概要
/Course outline
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. 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.


Back
開講学期
/Semester
2019年度/Academic Year  3学期 /Third Quarter
対象学年
/Course for;
1st year , 2nd year
単位数
/Credits
2.0
責任者
/Coordinator
Saji N. Hameed
担当教員名
/Instructor
Saji N. Hameed, Naohito Nakasato
推奨トラック
/Recommended track
履修規程上の先修条件
/Prerequisites

更新日/Last updated on 2019/02/15
授業の概要
/Course outline
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


Back
開講学期
/Semester
2019年度/Academic Year  3学期 /Third Quarter
対象学年
/Course for;
1st year , 2nd year
単位数
/Credits
2.0
責任者
/Coordinator
Takeaki Sampe
担当教員名
/Instructor
Takeaki Sampe
推奨トラック
/Recommended track
履修規程上の先修条件
/Prerequisites

更新日/Last updated on 2019/02/11
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
2. Viscosity, Laminar and turbulence, Reynolds number,
   Similarity of flow
3. Fluid statics, Hydrostatic balance, Buoyancy
4. First law of thermodynamics, Potential temperature
5. Eulerian/Lagrangian description of fluid motion, Streamline
6. Equation of continuity, Euler's equation of motion
7. Bernoulli’s theorem and its applications
8. Vorticity and stream function, Divergence and velocity
   potential, two-dimensional flow fields and complex functions
9. Navier-Stokes equation and related simple problems,
   Poiseuille flow
10. Dimensional analysis and Reynolds number
11. Characteristics of second-order partial differential
    equations, Wave/advection equation, Diffusion equation,
    Laplace/Poisson equation, Discretization of PDEs
12. Finite difference method, Lax’s equivalence theorem,
    CFL condition
13. Analysis of numerical stability, Numerical schemes for
    advection equation
14. Numerical schemes for diffusion equation and Laplace
    equation, 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.
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.


Back
開講学期
/Semester
2019年度/Academic Year  4学期 /Fourth Quarter
対象学年
/Course for;
1st year , 2nd year
単位数
/Credits
2.0
責任者
/Coordinator
Xiang Li
担当教員名
/Instructor
Xiang Li, Chunhua Su
推奨トラック
/Recommended track
履修規程上の先修条件
/Prerequisites

更新日/Last updated on 2019/02/15
授業の概要
/Course outline
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, “Digital signal processing”. 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 a computer; 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] 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
Quiz (20 points)
Homework (30)
Report (50 points)
履修上の留意点
/Note for course registration
Signal Processing and Linear System (undergraduate) or having learned an equivalent course is necessary.
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).


Back
開講学期
/Semester
2019年度/Academic Year  1学期 /First Quarter
対象学年
/Course for;
1st year , 2nd year
単位数
/Credits
2.0
責任者
/Coordinator
Yutaka Watanobe
担当教員名
/Instructor
Yutaka Watanobe, Yohei Nishidate
推奨トラック
/Recommended track
履修規程上の先修条件
/Prerequisites

更新日/Last updated on 2019/02/15
授業の概要
/Course outline
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.
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/


Back
開講学期
/Semester
2019年度/Academic Year  1学期 /First Quarter
対象学年
/Course for;
1st year , 2nd year
単位数
/Credits
2.0
責任者
/Coordinator
Yong Liu
担当教員名
/Instructor
Yong Liu
推奨トラック
/Recommended track
履修規程上の先修条件
/Prerequisites

更新日/Last updated on 2019/02/14
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


Back
開講学期
/Semester
2019年度/Academic Year  4学期 /Fourth Quarter
対象学年
/Course for;
1st year , 2nd year
単位数
/Credits
2.0
責任者
/Coordinator
Yong Liu
担当教員名
/Instructor
Yong Liu
推奨トラック
/Recommended track
履修規程上の先修条件
/Prerequisites

更新日/Last updated on 2019/02/14
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.


Back
開講学期
/Semester
2019年度/Academic Year  2学期 /Second Quarter
対象学年
/Course for;
1st year , 2nd year
単位数
/Credits
2.0
責任者
/Coordinator
Kazuyoshi Mori
担当教員名
/Instructor
Kazuyoshi Mori
推奨トラック
/Recommended track
履修規程上の先修条件
/Prerequisites

更新日/Last updated on 2019/02/15
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
Evaluation is based on submitted reports only, that is, "100%" evaluation is based on submitted reports.


Back
開講学期
/Semester
2019年度/Academic Year  4学期 /Fourth Quarter
対象学年
/Course for;
1st year , 2nd year
単位数
/Credits
2.0
責任者
/Coordinator
Mohamed Hamada
担当教員名
/Instructor
Mohamed Hamada
推奨トラック
/Recommended track
履修規程上の先修条件
/Prerequisites

更新日/Last updated on 2019/02/05
授業の概要
/Course outline
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.


Back
開講学期
/Semester
2019年度/Academic Year  1学期 /First Quarter
対象学年
/Course for;
1st year , 2nd year
単位数
/Credits
2.0
責任者
/Coordinator
Nobuyoshi Asai
担当教員名
/Instructor
Nobuyoshi Asai
推奨トラック
/Recommended track
履修規程上の先修条件
/Prerequisites

更新日/Last updated on 2019/02/08
授業の概要
/Course outline
his 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. Vector space and linear transformation

3. Vector space and linear transformation

4. Matrix Decompositions

5. Equivalent Decomposition

6. LDU Decomposition

7. Determinant and Inner Product

8. QR Decomposition

9. QR Decomposition

10. Shur Decomposition

11. Shur Decomposition

12. Jordan Decomposition

13. Jordan Decomposition

14. 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
Class activity, quizzes, and/or reports
履修上の留意点
/Note for course registration
Knowledge on the following classes are needed:
Linear Algebras I, II
Numerical Analysis


Back
開講学期
/Semester
2019年度/Academic Year  3学期 /Third Quarter
対象学年
/Course for;
1st year , 2nd year
単位数
/Credits
2.0
責任者
/Coordinator
Mohamed Hamada
担当教員名
/Instructor
Mohamed Hamada
推奨トラック
/Recommended track
履修規程上の先修条件
/Prerequisites

更新日/Last updated on 2019/02/05
授業の概要
/Course outline
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.



Back
開講学期
/Semester
2019年度/Academic Year  1学期 /First Quarter
対象学年
/Course for;
1st year , 2nd year
単位数
/Credits
2.0
責任者
/Coordinator
Shigeru Watanabe
担当教員名
/Instructor
Shigeru Watanabe
推奨トラック
/Recommended track
履修規程上の先修条件
/Prerequisites

更新日/Last updated on 2019/02/15
授業の概要
/Course outline
The purpose of this course is to give ideas of advanced analysis
for students who have deep understanding for the undergraduate
level mathematics  (Fourier analysis, complex analysis).
授業の目的と到達目標
/Objectives and attainment
goals
Students will be able to understand Fourier analysis as an introductory theory of function spaces.

Students will be able to understand an introduction to the theory of Hilbert spaces.

Students will be able to understand an introduction to special functions, and Fourier expansions by orthogonal polynomials.
授業スケジュール
/Class schedule
1-2. Reviews of undergraduate mathematics
3-6. Introductory theory of function spaces
7-10. Introduction to functional analysis
11-14. Fourier expansions by orthogonal polynomials
教科書
/Textbook(s)
Non
成績評価の方法・基準
/Grading method/criteria
Reports 100%
履修上の留意点
/Note for course registration
Prerequisites: Fourier analysis, complex analysis, general topology


Back
開講学期
/Semester
2019年度/Academic Year  3学期 /Third Quarter
対象学年
/Course for;
1st year , 2nd year
単位数
/Credits
2.0
責任者
/Coordinator
Lothar M. Schmitt
担当教員名
/Instructor
Lothar M. Schmitt
推奨トラック
/Recommended track
履修規程上の先修条件
/Prerequisites

更新日/Last updated on 2019/03/01
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.



Back
開講学期
/Semester
2019年度/Academic Year  3学期 /Third Quarter
対象学年
/Course for;
1st year , 2nd year
単位数
/Credits
2.0
責任者
/Coordinator
Kazuto Asai
担当教員名
/Instructor
Kazuto Asai
推奨トラック
/Recommended track
履修規程上の先修条件
/Prerequisites

更新日/Last updated on 2019/02/07
授業の概要
/Course outline
In this class, we deal with various topics arising from pure and applied mathematics concerning Algebraic Systems and Combinatorics. This year, we focus mainly on the theory of finite fields in the area of Algebraic Systems. The finite field F_q --- a field with finite (q) elements --- was first found by E. Galois, and so it is often called a Galois field GF(q). The structure of F_q as a field is uniquely determined by the number of elements q, and for the existence of F_q, it is necessary and sufficient for q to be a power of a prime p.

Beginning with polynomial rings, we overview the following: prime fields, finite polynomial fields, field extensions, splitting fields, structure of finite fields, primitive elements, Frobenius cycles, cyclotomic polynomials, and functions between finite fields. When we consider numbers, they are often supposed to be real or complex numbers. But here, we shall contact with more abstract numbers, rings, and fields, to develop the ability of mathematical and abstract thinking.

Finite fields are purely mathematical objects. For example, mathematicians often try to extend or modify the theory over real/complex numbers to the one over finite fields in their researches. Finite fields, however, have many application such as theory of experimental design, codes, and logical circuits, etc, which is the reason why the finite fields are very important objects for both of Scientists and Engineers.
授業の目的と到達目標
/Objectives and attainment
goals
Polynomial rings, Prime fields, Positive characteristic, Homomorphisms and isomorphisms, Field extension, Splitting fields, Uniqueness of the q-element field, Structure of finite fields, Primitive elements, Frobenius cycles, and Cyclotomic polynomials.

授業スケジュール
/Class schedule
1. Polynomial rings.
2. Prime fields F_p.
3. Homomorphisms and isomorphisms.
4. Finite polynomial fields and field extensions.
5--6. Finite fields.
7--8. Structure of finite fields.
9--10. Primitive elements.
11--12. Frobenius cycles.
13--14. Cyclotomic polynomials.
教科書
/Textbook(s)
1. Handout: 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. Kumiawaserironto Sonoouyou (1979), Iwanami Zensho 316, by I. Takahashi (in Japanese)
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: Applied Algebra, Linear Algebra 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/ (Directory for the class)


Back
開講学期
/Semester
2019年度/Academic Year  3学期 /Third Quarter
対象学年
/Course for;
1st year , 2nd year
単位数
/Credits
2.0
責任者
/Coordinator
Michio Honma
担当教員名
/Instructor
Michio Honma
推奨トラック
/Recommended track
履修規程上の先修条件
/Prerequisites

更新日/Last updated on 2019/02/15
授業の概要
/Course outline
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


Back
開講学期
/Semester
2019年度/Academic Year  3学期 /Third Quarter
対象学年
/Course for;
1st year , 2nd year
単位数
/Credits
2.0
責任者
/Coordinator
Akira Fujitsu
担当教員名
/Instructor
Akira Fujitsu
推奨トラック
/Recommended track
履修規程上の先修条件
/Prerequisites

更新日/Last updated on 2019/02/22
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


Back
開講学期
/Semester
2019年度/Academic Year  4学期 /Fourth Quarter
対象学年
/Course for;
1st year , 2nd year
単位数
/Credits
2.0
責任者
/Coordinator
Igor Lubashevskiy
担当教員名
/Instructor
Igor Lubashevskiy
推奨トラック
/Recommended track
履修規程上の先修条件
/Prerequisites

更新日/Last updated on 2019/02/22
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.


Back
開講学期
/Semester
2019年度/Academic Year  2学期 /Second Quarter
対象学年
/Course for;
1st year , 2nd year
単位数
/Credits
2.0
責任者
/Coordinator
Yen Neil Yuwen
担当教員名
/Instructor
Yen Neil Yuwen
推奨トラック
/Recommended track
履修規程上の先修条件
/Prerequisites

更新日/Last updated on 2019/02/20
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
 Sociology of science and technology
 Epistemology
 Social construction of technology
This course will give an introduction that covers a wide range of theories, techniques, applications to the well-designed human-computer systems, including:

Week 1-2. Activity/Category theory
Week 3-4. Distributed cognition
Week 5-6. Situated action
Week 7. Midterm check
Week 8-9. Participatory design
Week 10-11. Action research
Week 12-13. Emerging computing paradigms with HCC
Week 14. Examples on HCC researches (to each topics above)
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%
Quizzes: 10%
Test: 20%
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


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

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