AY 2016 Graduate School Course Catalog AY 2016 Graduate School Course Catalog AY 2016 Graduate School Course Catalog
| 2017/01/30 |
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| 開講学期 ／Semester |
2016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 |
Takafumi Hayashi |
| 担当教員名 ／Instructor |
Takafumi Hayashi , Yodai Watanabe , Akihito Nakamura |
| 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 | 2016/02/01 |
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| 授業の概要 ／Course outline |
Information security is essential to ensure the availability, integrity, and confidentiality of the computer services, computing systems, and networks. This course provides the introduction to the information security. |
| 授業の目的と到達目標 ／Objectives and attainment goals |
／Objectives and attainment goals The goal for students in this course is to learn the fundamentals of information security, including: + Principles of information security + information security management + Basic cryptography + Secure networkomg + Malicious codes + Societal issues in information security: legal, ethical, governmental + Enterprise Architecture The followings will be discussed in each lecture • Analysis of the tradeoffs of balancing key security properties (Confidentiality, Integrity, Availability). • The concepts of risk, threats, vulnerabilities and attack vectors including the fact that there is no such thing as perfect security) •The concept of trust and trustworthiness. • Ethical issues to consider in computer security, including ethical issues associated with fixing or not fixing vulnerabilities and disclosing or not disclosing vulnerabilities. • The principle of least privilege and isolation as applied to system design. • The principle of fail-safe and deny-by-default. • Avoiding to rely on the secrecy of design for security but also that open design alone does not imply security • End-to-end data security. • Multiple layers of defenses. • Security as a consideration from the point of initial design and throughout the lifecycle of a product. • Costs and tradeoffs imposed by security • Input validation and data sanitization in the face of adversarial control of the input channel. • Type-safe language like Java, in contrast to an unsafe programming language like C/C++. • Common input validation errors, and write correct input validation code. ‣ Human Error. • Using a high-level programming language with preventing a race condition from occurring and how to handle an exception. • The identification and graceful handling of error conditions. • The concept of mediation and the principle of complete mediation. • Standard components for security operations, instead of re-inventing fundamentals operations. • The concept of trusted computing including trusted computing base and attack surface and the principle of minimizing trusted computing base. • The usability in security mechanism design. • Security issues which can arise at boundaries between multiple components. • Toles of prevention mechanisms and detection/deterrence mechanisms. • The risks with misusing interfaces with third-party code and how to correctly use third-party code. • Updating software to fix security vulnerabilities and the lifecycle management of the fix. • Direct and indirect information flows. • Likely attacker types against a particular system. • The limitations of malware countermeasures (eg, signature-based detection, behavioral detection). • Identify instances of social engineering attacks and Denial of Service attacks. + Identification, Authentication and Attribute Confirmation • Identification and Mitigation of Denial of Service attacks • Risks to privacy and anonymity in commonly used applications. • The concepts of covert channels and other data leakage procedures. • The different categories of network threats and attacks. • The virtues and limitations of security technologies at each layer of the network stack. • Identify the appropriate defense mechanism(s) and its limitations given a network threat. • Security properties and limitations of other non-wired networks. • Cipher, cryptanalysis, cryptographic algorithm, and cryptology and describe the two basic methods (ciphers) for transforming plain text in cipher text. • The cryptographic primitives and their basic properties. Additional topics may be covered, depending on the interests of the students |
| 授業スケジュール ／Class schedule |
L1. Intorduction What is information Security The role of information security Academic, industrial, and social area related with information security History of information secutiy L2 Fundamental concepts - Goal of information security - Risk, threat, vulnerability, and control - Confidentiality, integrity, availability and related properties - Attack paradigm and protection paradigm - Mindsets - Defense in dept L3 Vulnerability management - Classes of vulnerabilities - Trends of vulnerability - Vulnerability management life cycle - Vulnerability assessment data model - Open standards for vulnerability management L4 Risks of Web applications and controls - Best practices - Critical web application security risks - Critical security controls - Assessment tools L5. Public key encryption schemes Definition of public key encryption schemes Examples of public key encryption schemes L6, 7. Security notions for 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) L8, 9. Relation among security notions Equivalence between SS and IND NM implies IND, IND-CCA2 implies NM-CCA2 Some separations L10, 11. Enterprise Architecture and information security The basics of Enterprise architecture (EA) and the applications of EA to information security Frameworks of Enterprise architecture Enterprise security planning Total security Total cost optimization L12 Information Security Management -The role of security policy - Standard on IT security policy - Project Management for Information Security L13. Cloud computing, Service Oriented Architecture and information security Security and Privacy in Public Cloud Computing Loosely Coupled Integration of Clouds Social issues L14 Cyber Security L15. Final review |
| 教科書 ／Textbook(s) |
No particular textbook is specified. |
| 成績評価の方法・基準 ／Grading method/criteria |
Assignments |
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| 開講学期 ／Semester |
2016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 |
Shuxue Ding |
| 担当教員名 ／Instructor |
Shuxue Ding , Takafumi Hayashi |
| 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 | 2016/01/25 |
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| 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 |
2016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 |
Toshiro Watanabe |
| 担当教員名 ／Instructor |
Toshiro Watanabe , Takahiro Tsuchiya |
| 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 | 2016/01/28 |
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| 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(³ÎΚ€ÎÉüœ¬ ) I,II,III. Sample distributions(ÉžËÜʬÉÛ) I,II. Estimation and Test of mean and variance I,II. Test of goodness of fit I,II. Linear regression I,II. Analysis of variance I,II. Stochastic processes 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 |
Prerequisite 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.) |
Ref. Books Feller, W. : An Introduction to Probability Theory, Vol.1, (Wiley) Statistics (Tokyo Univ Press) |
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| 開講学期 ／Semester |
2016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 |
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 | 2016/01/15 |
|---|---|
| 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 |
1. Mathematical Realization of Quantum Mechanics 2. Basic Concepts of Quantum Mechanics 3. EPR pair and measurement 4. Quantum Gates 5. Theory of Information and Communication 6. Quantum Computation 7. Shor’s Factorization Algorithm 8. Quantum Error-Correction 9. Quantum Cryptography 10. Quantum Algorithm |
| 教科書 ／Textbook(s) |
Useful books (Explained in the first 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) |
| 成績評価の方法・基準 ／Grading method/criteria |
Attendance and reports |
| 履修上の留意点 ／Note for course registration |
It is recommended to have basic knowledge of quantum mechanics. However, the course will accept students who do not have any knowledge of quantum mechanics but have strong desire to learn new knowledge |
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| 開講学期 ／Semester |
2016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 , Takafumi Hayashi |
| 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 | 2016/02/01 |
|---|---|
| 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 |
2016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 | 2016/01/27 |
|---|---|
| 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 |
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| 開講学期 ／Semester |
2016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 , Takafumi Hayashi |
| 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 | 2016/01/27 |
|---|---|
| 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 |
2016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 | 2016/03/15 |
|---|---|
| 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. Julia, which is a high-level, high-performance dynamic programming language for technical computing (http://julialang.org/). |
| 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/NMS2015 Julia Language : http://julialang.org/ |
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| 開講学期 ／Semester |
2016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 |
Hameed Saji N. |
| 担当教員名 ／Instructor |
Hameed Saji N. , 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 | 2016/01/25 |
|---|---|
| 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. We expect to spend some time in introducing students to languages for the many-core architectures such as OpenCL and CUDA as well. 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 |
2016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 , Hameed Saji N. |
| 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 | 2016/01/21 |
|---|---|
| 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 properties of fluid and principles of fluid dynamics, and learn how to solve some simple problems of fluid dynamics with numerical integration of the governing equations. |
| 授業スケジュール ／Class schedule |
1. Introduction, Properties of fluids, Units 2. Fluid statics, Equation of state, Hydrostatic balance 3. Dynamics of fluid, Eulerian/Lagrangian description of fluid motion, Streamline, Conservation of mass 4. Euler’s equation of motion, Bernoulli’s theorem, Vorticity and circulation, Divergence and velocity potential 5. Navier-Stokes equation and its application to some simple problems 6. Dimensional analysis, Reynolds number, Similarity of flow 7. Characteristics of 2nd order partial differential equations, Wave equation, Diffusion equation, Laplace equation 8. Discritization of differential equations, Consistency, Stability, Convergence 9. Finite difference method, CFL condition 10. Various numerical schemes for integration and numerical stability 11. Application of numerical methods to simulations of flow |
| 教科書 ／Textbook(s) |
No textbooks. |
| 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 |
・ Calculus (undergraduate) ・ Linear algebra (undergraduate) ・ Dynamics (undergraduate) ・ Numerical analysis (undergraduate) ・ Numerical modeling and simulations (graduate course) |
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| 開講学期 ／Semester |
2016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 | 2016/01/28 |
|---|---|
| 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 |
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| 開講学期 ／Semester |
2016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 |
Takafumi Hayashi |
| 担当教員名 ／Instructor |
Takafumi Hayashi |
| 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 | 2016/02/01 |
|---|---|
| 授業の概要 ／Course outline |
This course presents the basics of finite field, combinatrics based on finite field. This course also provide the topics on sequence design, block design, and their applications to computer science and engineering, especially communications, instrumentation, and cryptography. |
| 授業の目的と到達目標 ／Objectives and attainment goals |
This object of this course will provide the perspective of the combinatorics and its application to computer science and science. |
| 授業スケジュール ／Class schedule |
L1: Basics of combinatorics. What is combinatorics. The applications of combinatrics to computer science and engineering. L2: Finite Field Structure of a finite field F_p and its subfields. Construction of a finite field F_p for a prime number p. Galois Field. L3-4: Extension of a Finite field. Structure of the extended finite field F_q, for q=p^m. Primitive polynomial for a finite field. Applications of finite field. L5: Finite Geometry and its applications 1 Affine plane and finite field. Cyclic construction of points on an affine plane. L6: Finite Geometry and its applications 2 Projective geometry and finite field. L7: Experimental Design Applications of experimental design to computer science and engineering. History and examples of experimental design. Examples of Experimental design. L8-9: Block Design Algorithms of Block Design Construction. Applications of block design to image processing, data mining, and networking. L10: Block design and combinatrics. Block design for various problems of combinatrocis. Examples of balanced incomplete block design. L11: Latin Square Construction of Latin squares. Applications of Latin Square to communications, instrumentations, and cryptography. L12: Cyclic Difference Sets Structure of difference sets. Construction of cyclic difference set. Applications of cyclic difference set to sequence design, communication, radar, ultrasonic imaging and positioning. Cyclic difference set and conctucion of Hadamard matrix. L13-14 Sequence design Structure, properties, and construction of shift register sequence, perfect sequence, error-correction sequence, and zero-correlation zone sequence set. L15 Number theory and its application to computer science. Selected topics and related algorithms. |
| 教科書 ／Textbook(s) |
N/A |
| 成績評価の方法・基準 ／Grading method/criteria |
Assingments |
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| 開講学期 ／Semester |
2016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 | 2016/01/28 |
|---|---|
| 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. |
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| 開講学期 ／Semester |
2016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 |
Keitaro Naruse |
| 担当教員名 ／Instructor |
Keitaro Naruse |
| 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 | 2016/02/02 |
|---|---|
| 授業の概要 ／Course outline |
We see a number of evolving networks in our daily life, for example, hyperlink graphs in Web pages, a friend network, and so on. They look like a complete random graph. However, it has been revealed that they are in a special class of random graphs. For example, in a hyperlink graph in Web pages, the number of incoming links to each of the Web pages does not follow a normal distribution, but it distributes as the power law, in which most of the Web pages have only a small number of incoming links meanwhile some Web pages posses a huge number of links. The property is called "scale-free", and it has been shown mathematically that a scale-free network can be generated through both of "network growth" and "preferential attachment". Similarly, in a friend network, even each of you have a limited number of friends and most of the friends have same people in common, the whole friend network has a small diameter (around 6 to 7). The property is called "small world", and it is characterized as a graph with a large clustering coefficient and a short average path length in the graph theory terminology. And the small world graph can be generated by introducing a small randomness into a regular graph. |
| 授業の目的と到達目標 ／Objectives and attainment goals |
This course provides the theory of the evolving network, in particular, properties and generative mechanisms, and where we can find them in natural and artificial networks. Furthermore, the graph theory and the statistical theory are studied as well. |
| 授業スケジュール ／Class schedule |
#1 Introduction #2 Scale-free networks: Overview #3-4 Scale-free networks: Mathematical models #5 Scale-free networks: Web networks #6 Scale-free networks: Errors and tolerances #7 Small world networks: Overview #8-9 Small world networks: Mathematical models #10 Human dynamics: Message bursts #11 Human dynamics: Global cascades #12-13 Human dynamics: Recommendation systems #14 Latest issues #15 Summary |
| 教科書 ／Textbook(s) |
None |
| 成績評価の方法・基準 ／Grading method/criteria |
By reports on numerical experiments on evolving networks, and oral presentation on related works. |
| 履修上の留意点 ／Note for course registration |
None |
| 参考(授業ホームページ、図書など) ／Reference (course website, literature, etc.) |
http://iplab.u-aizu.ac.jp/moodle/ |
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| 開講学期 ／Semester |
2016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 |
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 | 2016/01/27 |
|---|---|
| 授業の概要 ／Course outline |
This course is concerned with the multidimensional systems theory. This theory includes multidimensional control system and image processing, and so on. We will proceed precisely with mathematical descriptions. |
| 授業の目的と到達目標 ／Objectives and attainment goals |
This course is concerned with the multidimensional systems theory. This theory includes multidimensional control system and image processing, and so on. We will proceed precisely with mathematical descriptions. |
| 授業スケジュール ／Class schedule |
1. Scalar 2-D Input/Output Systems 2. Stability 3. Structural Stability 4. Multi-Input/Multi-Output Systems 5. Stabilization of Scalar Feedback Systems 6. Characterization of Stabilizers for Scalar Systems 7. Stabilization of Strictly Causal Transfer Matrices 8. Characterization of Stabilizers for MIMO Systems 9. Stabilization of Weakly Causal Systems 10. Stabilization of MIMO Weakly Causal Systems |
| 教科書 ／Textbook(s) |
1. Multidimensional Systems Theory (2nd Ed). D.Reidel Publishing, 2003. (Reference) 2. Schaum's Outline of Theory and Problems of Signals and Systems 3rd Ed.(Schaum's Outlines) (Reference) |
| 成績評価の方法・基準 ／Grading method/criteria |
Final examination and/or Reports |
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| 開講学期 ／Semester |
2016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 | 2016/01/29 |
|---|---|
| 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. |
| Back |
| 開講学期 ／Semester |
2016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 | 2016/01/29 |
|---|---|
| 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. |
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| 開講学期 ／Semester |
2016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 | 2016/01/17 |
|---|---|
| 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. Reviews of undergraduate mathematics 2. Introductory theory of function spaces 3. Introduction to functional analysis 4. Fourier expansions by orthogonal polynomials |
| 教科書 ／Textbook(s) |
Non |
| 成績評価の方法・基準 ／Grading method/criteria |
Reports every class |
| 履修上の留意点 ／Note for course registration |
Prerequisites: Fourier analysis, complex analysis |
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| 開講学期 ／Semester |
2016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 | 2016/02/02 |
|---|---|
| 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. |
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| 開講学期 ／Semester |
2016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 | 2016/01/27 |
|---|---|
| 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. Finite fields. 6. Structure of finite fields. 7. Primitive elements. 8. Frobenius cycles. 9. 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 |
By presentation and reports. |
| 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) |
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| 開講学期 ／Semester |
2016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 | 2016/01/15 |
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| 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 |
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| 開講学期 ／Semester |
2016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 | 2016/01/20 |
|---|---|
| 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. |
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| 開講学期 ／Semester |
2016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 | 2016/01/28 |
|---|---|
| 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. |
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| 開講学期 ／Semester |
2016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 , 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 | 2016/01/18 |
|---|---|
| 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. |
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| 開講学期 ／Semester |
2016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 | 2016/02/02 |
|---|---|
| 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 |