This course introduces students to the common law trust, famously described by Maitland as ‘the most distinctive achievement of English lawyers’. The trust is a device which allows for the management of rights, both personal and proprietary, for the benefit of others or for certain permitted purposes. So, for example, a person might set up a trust to manage rights for his or her infant children, for the purposes of investment, for securitisation, to relieve poverty in a particular city, and many other purposes. In this respect, the trust is an extremely flexible instrument, which is now being copied in many civil law jurisdictions. The topics to be covered are as follows: 1. What is a trust? 2. Principal categories of trusts 3. The uses of trusts 4. Creating trusts 5. Trusts for purposes 6. Limits of trusts 7. The administration of trusts 8. The position of settlors and beneficiaries 9. Remedies for breach of trust 10. The role of the court 11. Position of third parties TA ：陳詠(Sandy Chen) E-mail ：R02A41010@ntu.edu.com The course aims to help students understand: (1) the core principles, topics, and cases of the common law trust; (2) the legal methodology employed by common law jurists; (3) the legal style of the common law tradition. College of Law Main Campus Dear students, For those who want to take the course of [Introduction to Common Trust Law] but didn’t get enrolled in the class in the first place, please come to get the registration code for the course from TA Sandy in room 2406 on the 4th floor of Wan Tsai Research Hall (the same building of where the law library is) during 9:00~12:00 in the morning on Feb. 20 (Mon.). We allow 20 more students to get enrolled in the class. TA Sandy William Swadling 58 Intensive courses LAW7605 (A21EM0760) 1 (College of Law) Graduate Institute of Law http://www.law.ntu.edu.tw/main.php?site_id=1

In Germany, constitutional law and the jurisprudence of the Federal Constitutional Court are of paramount importance to the legal system and to the political process. Besides, various elements of German constitutional law have heavily influenced the development of constitutional law in other countries. The course aims at providing a concise overview of the structures and contents of German constitutional law (branches of government, democracy, rule of law, social state principle, fundamental rights etc.). Also, the intricate relationship of constitutional law to public international law and to European Union law will be investigated. TA ：陳冠中(Kuan-Chung Chen) E-mail ：r02a21038@ntu.edu.tw The objective is to provide a concise overview of the structures and contents of German constitutional law. College of Law Main Campus Hanno Kube 30 Intensive courses LAW7606 (A21EM1500) 1 (College of Law) Graduate Institute of Law http://www.law.ntu.edu.tw/main.php?site_id=1

Part I: Virtual Reality 1. Look real, sound real, feel real, smell real, react realistically and in real-time 2. 3D Sound, directional sound 3. Environment Walkthrough, Distributed Interactive Simulation (DIS) 4. Tracking devices: space tracker, tracking algorithms 5. Immersive display: Head Mounted Display, BOOM, Stereo shutter glasses 6. Force Feedback Devices (Joystick, PHANToM etc.) 7. Trajectory prediction algorithms Part II: Display and Visualization 1. Modeling (Solid modeling, build large models, physically based modeling, motion dynamics) 2. Global illumination algorithms( radiosity, volume rendering, scientific sualization) 3. Texture mapping and advanced animation 4. Graphics packages : OpenGL (X window, WinXP), DirectX(WinXP) Part III: Hardware and accelerators 1. High performance graphics architectures (Pixel-Planes, Pixel Machine, SGI reality engine, PC Graphics (nVidia, ATI), Accelerator Chips & Cards) Part IV: Virtual reality paper survey and term project 1. To understand VR technology. 2. Can do a VR project, including writing a software that can be executed in a NB or mobile smartphone/Pad (Apple or Android). 3. Can read related papers and comments on the pros and cons of these papers. Virtual reality (VR), the use of computer modeling and simulation that enables a person to interact with an artificial three-dimensional (3-D) visual or other sensory environment. VR applications immerse the user in a computer-generated environment that simulates reality through the use of interactive devices, which send and receive information and are worn as goggles, headsets, gloves, or body suits. In a typical VR format, a user wearing a helmet with a stereoscopic screen views animated images of a simulated environment. The illusion of “being there” (telepresence) is effected by motion sensors that pick up the user’s movements and adjust the view on the screen accordingly, usually in real time (the instant the user’s movement takes place). Thus, a user can tour a simulated suite of rooms, experiencing changing viewpoints and perspectives that are convincingly related to his own head turnings and steps. Wearing data gloves equipped with force-feedback devices that provide the sensation of touch, the user can even pick up and manipulate objects that he sees in the virtual environment. College of Electrical Engineering & Computer Science Main Campus This course will be graded by 1. (1/3) Two homeworks, 2. (1/3) one midterm, and 3. (1/3) one final project. Ming Ouh Young 50 Monday 7,8,9 CSIE7633 (922EU1940) 3 (College of Electrical Engineering and Computer Science) Graduate Institute of Computer Science & Information Engineering,
(College of Electrical Engineering and Computer Science) Graduate Institute of Networking and Multimedia
*Registration eligibility: juniors and above.
http://www.csie.ntu.edu.tw/main.php?lang=en

Optimization techniques are used in all kinds of machine learning problems because in general we would like to minimize the testing error. This course will contain two parts. The first part focuses on convex optimization techniques. We discuss methods for least-squares, linear and quadratic programs, semidefinite programming, and others. We also touch theory behind these methods (e.g., optimality conditions and duality theory). In the second part of this course we will investigate how optimization techniques are applied to various machine learning problems (e.g., SVM, maximum entropy, conditional random fields, sparse reconstruction for signal processing applications). We further discuss that for different machine learning applications how to choose right optimization methods. learn how to use optimization techniques for solving machine learning problems. College of Electrical Engineering & Computer Science Main Campus Chih-Jen Lin 80 Tuesday 2,3,4 CSIE7435 (922EU3940) 3 (College of Electrical Engineering and Computer Science) Graduate Institute of Networking and Multimedia,
(College of Electrical Engineering and Computer Science) Graduate Institute of Computer Science & Information Engineering http://www.csie.ntu.edu.tw/main.php?lang=en

[Course description] Control is the action of causing a system variable to approach some desired value. It is also a fundamental and universal problem-solving approach in many traditional and interdisciplinary fields. A control system, in a very general sense, is a system with an (reference) input that can be applied per the desired value and an output from which how well the system variable matches to the desired value (e.g., errors) can be determined. It can be found in daily life, almost all engineering disciplines, and even biological and social studies. For examples, bicycle riding involves with a control system comprising of a bicycle and a rider, with inputs and outputs associated with the desired attitude, speed, and direction of the bicycle. Temperature control systems have applications in household, automobile, aerospace, office, factory, and agriculture environments. Motion control systems are critical to factory automation and precision instruments, such as industrial robots, atomic-force microscopes, and step-and-scan photolithography exposure systems. Many modern cameras equip with autofocus and vibration compensation systems to minimize image blur. Many kinds of circuits such as phase lock loops, operational amplifiers, and voltage regulators rely on control to ensure their functions and performance. A living body is a complex control system where many critical variables such as heart beat rate, blood pressure, and body temperature are regulated constantly for health. Central banks of most countries around the world set interest rates as a way to control inflation. This undergraduate course is designed for junior and senior (3rd/4th yr.) students to apprehend basic modeling, simulation, analysis, and design techniques for control systems. It intends to cover fundamentals of “classical control” that primarily focuses on frequency domain feedback control approaches for single-input-single-output linear dynamical systems. When time permits, some essential elements in modern-day control engineering such as state-space approaches, discrete-time digital control, and numerical methods will also be introduced. [Course goals] Basic: – Awareness of the strength and the importance of control systems, especially the effectiveness of feedback – Ability of deriving dynamic models and simulating dynamic responses – Ability of analyzing and designing feedback controllers for linear SISO systems in the frequency domain using root locus and frequency response techniques Bonus: – Awareness of some advanced control topics (e.g., state-space methods, digital control, and nonlinear systems) – Development of technical writing skills in English College of Electrical Engineering & Computer Science Main Campus [Prerequisites] Linear algebra, ordinary differential equations, Laplace transforms, fundamental circuit and mechanics analysis — which should have been well covered by several freshman and sophomore (1st/2nd yr.) courses in most electrical and mechanical engineering curriculums. Prior exposure to the analysis of signals and systems will be beneficial but not absolutely required. Kuen-Yu Tsai 60 Thursday 7,8,9 EE3024 (901E43100) 3 Non-degree Program: Education Program For Agricultural Automation,
(College of Electrical Engineering and Computer Science) Department of Electrical Engineering,
Non-degree Program: Transprotation Electrification Technology Program http://www.ee.ntu.edu.tw/en/

This course is on discrete mathematics. It covers combinatorics, boolean logic, computation theory, analysis of algorithms, probability, algebra, number theory, graph theory, set theory, and many other fields. Parts of the book should have been covered in high school and will be skipped or only briefly reviewed. I have in mind basic combinatorics, logic, and basic set theory. This courses prepares students for foundations of computer science and analysis of algorithms. It is also useful for many applications of computers and mathematics, even social sciences. College of Electrical Engineering & Computer Science Main Campus Homeworks. Examinations. Yuh-Dauh Lyuu 50 Thursday 2,3,4 CSIE2122 (902E25200) 3 *Majors-only (including minor and double major students).

(College of Electrical Engineering and Computer Science) Department of Computer Science & Information Engineering http://www.csie.ntu.edu.tw/main.php?lang=en

This course is mainly for graduted students (but not restricted to). We will provide techniques to estimate unknown system parameters, and design the controller for such systems. The main topics are: -Introduction -Identification of System Parameters -Adaptive Control of Linear Systems -Adaptive Control of a Class of Nonlinear Systems -Adaptive Neural Network Control -Adaptive Sliding Mode Control The main objectives are: – Estimate unknow system parameters – Design Adaptive controllers for linear and nonlinear sytems – Analysis of system properties for systems with unknown parameters – Apply the adaptive control techniques to various systems College of Electrical Engineering & Computer Science Main Campus Evaluation: -Homework (every 2~3 weeks) -Final term report -Final oral presentation Li-Chen Fu 28 Wednesday 2,3,4 EE7005 (921EM1380) 3 (College of Electrical Engineering and Computer Science) Graduate Institute of Electrical Engineering http://www.ee.ntu.edu.tw/en/

.1.Humanoid Robotics — Definition of walking, dynamic and static gaits — ZMP (zero moment point) — Lagrange’s

Equations — Control Algorithms of Humanoid Robotics — Sensors and Sensing Aspects of Humanoid Robotics

2.Mutisensor Fusion and Integration –Sigal Level Fusion –Pixel Level Fusion –Feature Level Fusion –Decision Level

Fusion –Kalman Filter –Extended Kalman Filter –Particle Filter –Covariance Intersection –Covariance Union —
Dempster-Shafer Evidence Theory 3.Sensing and Control for Robot Motion 4.Interactive Service Robotics
5.Advanced Topics on Robot Sensing and Control 6.Practical examples of robot sensing and control through photos and video demonstrations. The objectives of this course are to let students who have had the basic background of the robot sensing and control issues, approaches with more in depth understanding of theories and practical applications in robot sensing

and control. The idea of this course is to convey the concept that usually sensing and control should not be

separated and they are interdependent in dealing with an intelligent systems, such as an intelligent robotics system.

Firstly, student will learn more advanced robot sensing and control issues in humanoid robot including definition of

walking, dynamics static gaiting issues, control algorithms and the need for robot sensors interact with different

control aspects. The second focus will be the study of advanced issues of sensor fusion and integration.

Synergistic use of multiple sensors by machines and systems enables greater intelligence to be incorporated into

their overall operation. Motivation for using multiple sensors can be considered as response to simple question:

if a single sensor can increase the capability of a system, would the use of more sensors increase it even further ?

In this course, theories of multisensory fusion and its applications to sensory controlled robotics systems which

involves mathematical and statistical issues including combining sensor uncertainty methods for sensor fusion

includes estimation methods, such as covariance Intersection (CI), Kalman Filtering; Classification methods,

such asSupport Vector Machine (SVM) etc. will be presented and discussed. The third focus will be the advanced

robot motionplanning and control issues. The fourth focus will be the advanced topics in interactive service robotics
by using various sensing and control algorithms.Finally, a variety of practical examples of robot sensing and control

will be presented through photos and video demonstrations. After taking this course, it is expected that students

will l get the state of the art knowledge about the advanced core robotics technologies especially in robot sensing

and control. College of Electrical Engineering & Computer Science Main Campus This course is suitable for senior and graduate students. There is a take home project in addition to the weekly class meets. The final grade will be computed on the basis of the following weights: Take Home Project Report. 25% Project Presentation during the Class. 25% Midterm Exam. 25% Final Exam. 25% TOTAL 100% Ren C. Luo 20 Thursday A,B,C EE5155 (921EU4350) 3 (College of Electrical Engineering and Computer Science) Graduate Institute of Electrical Engineering http://www.ee.ntu.edu.tw/en/

This is an introductory course in system identification, the process of developing or improving a mathematical representation of a physical system using experimental data. This course focuses equally on theoretical and practical aspects of the subject. Students will learn key mathematical skills including linear time-invariant systems, random processes, and basic estimation techniques. Practical system identification skills such as input signal design, system excitation, and model validation will also be discussed. Students are required to integrate the knowledge into their works of final projects. 1. Review of linear systems 2. Random variables and random processes 3. Least-square estimation 4. Non-parametric model identification 5. Parametric model identification 6. State-space methods 7. System identification in practice 8. Advanced topics* (subspace identification, time varying or nonlinear systems) 9. Final project presentation College of Electrical Engineering & Computer Science Main Campus Undergraduate-level Control Systems, and/or Signal and Systems. Basic/working knowledge about linear algebra, linear dynamical systems, state-space models, and Fourier, Laplace, and Z-transforms. Kuen-Yu Tsai 15 Thursday 2,3,4 EE5129 (921EU8300) 3 (College of Electrical Engineering and Computer Science) Graduate Institute of Electronics Engineering,
(College of Electrical Engineering and Computer Science) Graduate Institute of Electrical Engineering,
Non-degree Program: Transprotation Electrification Technology Program http://www.ee.ntu.edu.tw/en/

1. Data Storage 2. Data manipulation 3. Operating systems 4. Networks and Internet 5. Programming Languages 6. Data and file Structures 7. Database structures 8. Artificial Intelligence . College of Electrical Engineering & Computer Science Main Campus Grading: 1.Homework: 30% 2.Midterm quiz: 30% 3.Final exam: 40% Prerequisite: Computer Programming Polly Huang 30 Tuesday 6 Wednesday 8,9 EE1003 (901E10110) 3 *Majors-only (including minor and double major students).
(College of Electrical Engineering and Computer Science) Department of Electrical Engineering http://www.ee.ntu.edu.tw/en/

Hazardous exposures are usually complex extended temporal processes leading to the development of biological responses, “damage/adverse responses/health effects”. A study intended to determine the quantitative relationship between exposure and risk of the effect requires a careful matching of the temporal variation in exposure with the kinetics of uptake, distribution and metabolism and matching those to the dynamics of response. However, bias and attenuation of the health risk estimate can be introduced when there is exposure error in air pollution measurement. Adequate exposure metrics may provide a means of reducing error (leading to less bias and uncertainty in health risk estimates) if they capture variability in exposure, which depends on the study design, health outcome, and pollutant of interest. To enable this, the course will start from a review of the basic components of exposure assessment for air pollution and subsequently introduce exposure metrics for four types of health outcomes: different combination of reversible/irreversible and discrete/proportional outcomes. Students will develop knowledge of exposure determinants and its temporal behavior (variability), in conjunction with skills for modeling temporal behavior of exposures and outcomes through simulations using excel spreadsheets. Guided critical analysis of publications will be performed, and information from simulations will be used to design an exposure assessment matched to the biology of the adverse effect(s). In the finals, the class will culminate with a design project where small groups of students design a new study of a specific exposure and hypothesized effect(s) reported in a previously critiqued scientific paper. The overall goal of this class is to develop the student’s ability to perform a biologically-based exposure assessment suited for testing an agent based hypothesis about a causal exposure-risk relationship in an epidemiological study. The specific learning objectives are: 1. The student’s knowledge base of exposure characteristics and assessment methods, and their application will be broadened through presentations, readings, critiques, and discussions of exposure assessment for environmental and occupational epidemiology. 2. The students will be introduced to a temporal modeling approach for simulating environmental and occupational exposures (exposure metrics), formulating a model of a linked exposure and health effects process as the basis for designing an epidemiologic study, and they will apply this approach to four different types of disease outcomes. 3. The students will be able to apply the knowledge and use their analytical skills to critique the exposure assessments and linkage with the health outcomes in selected publications. 4. Given a previously critiqued publication with a limited exposure assessment, the students will develop an improved study design using the temporal model approach that will provide a better test of the epidemiologic exposure-risk hypothesis, and present that approach to the class. College of Public Health Downtown Campus-College of Public Health The course sessions will include presentation on the topic of the day by the lecturer, and discussions of reading, review of homeworks, paper critiques, and other topics of interest. Wan-Chen Lee 12 Wednesday 6,7 EH5026 (844EU1360) 2 *Registration eligibility: juniors and above.
(College of Public Health) Graduate Institute of Environmental Health http://ieh.ntu.edu.tw/?locale=en

This course is offered for Information Systems majors, and the focus is naturally on the electronic aspect of marketing. Electronic marketing (e-marketing) is an area of study that combines marketing strategy with information technologies. It is one of the most significant developments in marketing in decades and represents an extremely dynamic area. E-marketing specifically addresses those marketing exchanges that are carried out, fully or partially, in an electronically networked marketplace. The primary form of instruction used in this course is the case method. Through the case method, you will be exposed to real-life situations that create a challenging learning environment in which you can share opinions and perspectives and learn from one another. Conceptual and real cases, international and local cases, and video and Web cases are all included. College of Management Main Campus Ming-Hui Huang 30 Thursday 7,8,9 IM3011 (705E33100) 3 (College of Management) Department of Information Management
*Registration eligibility: juniors and above. http://www.management.ntu.edu.tw/en/IM