Anglo-American Contract Law is a case-study course conducted in English. We use the following textbook: John P. Dawson et al., Contracts–Cases & Comment (Foundation Press: 10th edition, 2013). Students are required to read the assigned reading and brief the case if called upon. After each case is briefed by student, professor will guide the discussions. Students are encouraged to well prepare and participate discussion in the classes. Through the studies of cases of various topics under Anglo-American Contract Law, this course aims to teach students not only the important rules and principles of common law contracts but also the skill to read cases as well as issue-finding ability. Students will learn how to write brief and orally brief the case. Discussions provide students to think of viable arguments from different aspects and angles with justification and reasoning. College of Law Main Campus Prerequisite:
Civil Code-kinds of Obligations: LAW3281 (A01 37210) or LawILS7022 (A41 M0180).

Read the assigned reading (please see syllabus). Attend the classes. Brief the case if called upon. Participate discussion. A final exam will be conducted at the end of semester. Jen Guang Lin 30 Tuesday 1,2 LAW3360 (A01E39100) 2 (College of Law) Department of Law,
(College of Law) Department of Law, Legal Science Division http://www.law.ntu.edu.tw/main.php?site_id=1

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

This course introduces techniques that the student can use to construct simple models for analyzing and understanding the performance of systems that they are interested in. (1) To introduce students to analytical modeling of system performance. Computer systems are complex, making it hard to understand their behavior and predict their performance. Students will learn some mathematical techniques for modeling system performance, and exercise their modeling skill. (2) To broaden the student’s interest in Computer Science. Computer Science is increasingly multi-disciplinary; for example, data streams bring together issues in hardware, networking and databases. This course will give students broad exposure to analytical modeling in different areas of Computer Science. College of Electrical Engineering & Computer Science Main Campus Pre-requisites are Probability, Networks, OS Tay Young Chiang 20 Wednesday 2,3,4 CSIE5023 (922EU0240) 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
*Registration eligibility: seniors and above. http://www.csie.ntu.edu.tw/main.php?lang=en

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

Tentative Course Outline: 1. Experiments, Models, and Probabilities 1.1. Applying Set Theory to Probability 1.2. Probability Axioms 1.3. Some Consequences of the Axioms 1.4. Conditional Probability 1.5. Independence 1.6. Sequential Experiments and Tree Diagrams 2. Random Variables 2.1. Definitions 2.2. Probability Mass Function 2.3. Families of Discrete Random Variables 2.4. Cumulative Distribution Function (CDF) 2.5. Probability Density Function 2.6. Families of Continuous Random Variables 3. Random Variables and Expected Value 3.1. Conditional Probability Mass/Density Function 3.2. Probability Models of Derived Random Variables 3.3. Average 3.4. Variance and Standard Deviation 3.5. Expected Value of a Derived Random Variable Midterm exam 4. Random Vectors 4.1. Probability Models of N Random Variables 4.2. Vector Notation 4.3. Joint Cumulative Distribution Function 4.4. Joint Probability Mass/Density Function 4.5. Marginal PMF/PDF 4.6. Functions of Two Random Variables (Jacobian Transformation) 4.7. Conditioning by a Random Variables 4.8. Bivariate Gaussian Random Variables 4.9. Correlation Matrix 5. Sums of Random Variables 5.1. Expected Values of Sums 5.2. PDF of the Sum of Two Random Variables 5.3. Moment Generating Functions 5.4. MGF of the Sum of Independent Random Variables 5.5. Random Sums of Independent Random Variables 5.6. Central Limit Theorem 5.7. Applications of the Central Limit Theorem 5.8. The Chernoff Bound 6. Parameter Estimation Using the Sample Mean 6.1. Sample Mean: Expected Value and Variance 6.2. Deviation of a Random Variable from the Expected Value 6.3. Point Estimates of Model Parameters 6.4. Confidence Intervals 7. Hypothesis Testing 7.1. Significance Testing 7.2. Binary Hypothesis Testing Final exam To introduce to students the theory, models and analysis of probability and basic statistics and their applications with emphasis on electrical and computer engineering problems. College of Electrical Engineering & Computer Science Main Campus Calculus (A) 1 & 2
Grading: Homework : 20%, Midterm : 40%, Final : 40%, Participation 5% Shi Chung Chang 50 Monday 4 Thursday 8,9 EE2007 (901E21000) 3 (College of Electrical Engineering and Computer Science) Department of Electrical Engineering http://www.ee.ntu.edu.tw/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/

Principles of nonlinear optics with emphasis on the fundamental aspects of nonlinear optical theory and techniques. To understand the principles of nonlinear optics. To be equipped with the basic ability to analyze a nonlinear optics problem. College of Electrical Engineering & Computer Science Main Campus To understand the basic principles behind different nonlinear optics phenomena. Chi-Kuang Sun 30 Thursday 7,8,9 EE5050 (921EU2310) 3 (College of Electrical Engineering and Computer Science) Graduate Institute of Electrical Engineering,
(College of Electrical Engineering and Computer Science) Graduate Institute of Electro-Optical Engineering,
(College of Electrical Engineering and Computer Science) Graduate Institute of Biomedical Electronics and Bioinfornatics,
Non-degree Program: Program of Photonics Technologies 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/