UK and EU Company Law is a one-semester module which aims to provide an introduction to and analysis of some of the fundamental areas of UK and EU laws of companies. The module covers topics concerning the use of the corporate form, in particular looking at issues arising on incorporation, issues arising from the company’s structure, administration and management and issues with directors’ obligations and minority shareholder protection. It also covers corporate responsibility in contract, tort and criminal law and major issues of corporate governance in the UK and EU. TA ：李建德(Lee, Chien-Te ) E-mail ：sp.peterlee@gmail.com On successful completion of this module, students should be able to: Module-specific skills (1) demonstrate a good understanding of the main areas of company law in England and Wales and the EU, and be able to discuss the practical and business context in which they operate; (2) demonstrate critical awareness of relevant issues, and identify and analyse critically legal problems in the commercial law context; and (3) demonstrate awareness of pragmatic, commercial, moral, policy and/or other issues in this field. Discipline-specific skills (4) identify and evaluate critically legal data from more than one source or jurisdiction; (5) analyse and apply legal data to specific facts and deduce likely outcomes where law is indeterminate; (6) demonstrate independent legal research and study skills; and (7) identify, select and organise materials and produce coherent and convincing arguments. Personal and key skills (8) demonstrate effective written and oral communication skills; (9) demonstrate sound paper-based and electronic research skills; and demonstrate effective team skills. College of Law Main Campus students need to have good command of English as they will be required to do group presentation – starting from the second lesson of week1. Joseph Lee 40 Intensive courses LAW5212 (A21EU4420) 2 (College of Law) Graduate Institute of Law,
(College of Law) Department of Law
*Registration eligibility: juniors and above.
http://www.law.ntu.edu.tw/main.php?site_id=1

It will maintain a specific focus on how new tech and constitutional rights interact. Many examples will come from the US context, but the focus will be global, not exclusively America. Relevant US Supreme Court cases related to media law, as well as international law treaties and leading scholars’ articles, will play roles in enhancing students’ opportunities for analysis. In terms of content, the course will be divided into four main sections: background on basic ideas of freedom of the press, drawing from the US context but with a broader scope. Second, in-depth media law issues including net neutrality, content regulation, copyright, etc. Third will be a detailed investigation, including practical, business-oriented examples, of intellectual property law and its impact and influence. Finally, the course will conclude by looking at the future: artificial intelligence, environmental threats and opportunities, humans’ role in an increasingly technological world, etc. In this course, student work and assignments will not be limited to exclusively heavy reading of hundreds of pages of cases, translation of arcane and difficult passages, etc. The goal will be broad-based comprehension as well as cultivation of ability to think, discuss, and write critically about these important issues. The focus, of both readings as well as student writing, will be quality, not quantity. The teaching style of this course will: A) Be student-centered B) Comprehensive, and C) Ask why and how, not only what the law is. Students will need to speak and work in groups much more than potentially experienced in some other courses. And, as mentioned above in Evaluation, the semester grade will be determined by a midterm and final examination, as well as in-class participation and assignment work. Course content: Weeks 1-4: Freedoms of speech, assembly, and the press, focus on US constitutional law and broader related issues Weeks 5-8: Technical aspects of media law including content regulation, copyright, media law as a busin Two main goals: 1) Give students a broad-based understanding of key constitutional, economic, and human rights issues related to media law and new technology. 2) Improve students’ ability to analyze these topics in English. College of Law Main Campus Proficient English, law major and some comparative law experience preferred but not required Charles Wharton 50 Monday 6,7 LAW5247 (A21EU5100) 2 (College of Law) Graduate Institute of Law,
(College of Law) Department of Law
*Registration eligibility: juniors and above. 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

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/

The module will be delivered over one semester, as a blend of small group work and lectures, practical exercises, group project, presentation and in-class discussion of reading tasks. The aim of this course is to introduce concepts of study design, data collection and statistical analysis commonly used in public health research with a strong focus in global health. College of Public Health Downtown Campus-College of Public Health 1. Active participations in the discussion and presentation of reading tasks are requirements for all students. 2. On the completion of this course, students will identify a specific research topic related to global health and use the skills and knowledge taught in the course to undertake a critical review of the literature relating to the identified research topic/problem, design a study to investigate the problem, and write an appropriate protocol for conducting a research project on the topic, including ethical aspects of their research. 3. For the mid-term presentation, each student is required to do a 15-minutes presentation on her/his identified research topic. The content of presentation should include a preliminary report of background, literature search strategy and research hypotheses. 4. For the final presentation, each student is required to do a 15-minutes presentation on her/his research proposal for the identified topic. The content of presentation should include a report of background, literature review, research hypotheses, study design and statistical methods. 5. Each student is required to submit a final written report in the format of a research project proposal, including project title and sections on research background, literature review, materials and methods, and expected outcomes. Wei-Jane Chen 12 Wednesday 6,7,8 EPM8003 (849ED0400) 3 (College of Public Health) Graduate Institute of Epidemiology and Preventive Medicine
http://epm.ntu.edu.tw/?locale=en

The aim of this course is to provide a general introduction to path analysis, factor analysis, structural equation modeling and multilevel analysis. The examples and data are extensively drawn from literature in health and medical sciences. Students will learn how to use Mplus and Lisrel software to undertake these analyses. After attending the course, students should be able to describe the relationship between commonly used statistical methods and structural equation modeling (SEM); define the statistical concepts behind factor analysis, path analysis, and structural equation modeling; understand the relation between SEM and multilevel modeling (MLM); explain the above statistical methods and properly interpret their results; and use a computer software package to undertake the statistical analyses and correctly specify the statistical models. SEM has been very popular among quantitative social scientists in the last two decades, and has started to draw attentions from epidemiologists. SEM is a very useful tool for testing causal models, and learning SEM theory is very helpful for students to understand the causal assumptions behind different models. SEM is also useful for explaining the concepts of confounding, mediation and moderation in epidemiological research. The course will start with basic concepts of SEM, such as model specification, fitness testing, interpretation of causality and model modification. Then, more advanced topics will be introduced, such as equivalence models, identification issues, and multiple groups testing. MLM will then be introduced for the analysis of clustered data, where random effects may be viewed as latent variables. Students will be assessed by their participation in the classroom discussion, one interim and one final report on the critical appraisal of literature and real data analysis. By the end of this course, students should be able to: Describe the relations between general linear models and structural equation models Explain the statistical theory of principal component analysis, exploratory and confirmatory factor analysis, path analysis and structural equation models Understand the concepts and rationales of causal models within the framework of structural equation models Understand the concept of mediation and the decomposition of total effects into direct and indirect effects Undertake structural equation modeling using statistical software packages and interpret the results properly Report the results from structural equation modeling properly College of Public Health Downtown Campus-College of Public Health Active participation in class discussion and practical session is required. Tu, Yu Kang 30 Friday 3,4 EPM7001 (849EM0850) 2 (College of Public Health) Graduate Institute of Epidemiology and Preventive Medicine,
Common General Education Center Master Program In Statistics of National Taiwan University
http://epm.ntu.edu.tw/?locale=en

The aim of this course is to provide a general introduction to the research methods and application in global health. The examples and data are drawn from published literatures related to evidence-based medicine and health data research. The course will start with basic concepts of global health and evidence-based approach. Then, more advanced topics will be introduced, such as selection of a topic of interest, setting up the search strategy for literature review, and formation of a synthesis. Introduction to the management of public health data, as well as the assessment for quality of care using health claims data, will also be provided. Three special lectures will also be provided by experts in the relevant fields. Students will be guided to conduct projects related to their research, and present their results at the end of this semester. By the end of this course, students should be able to: 1. Understand the concepts and rationales of evidence-based medicine within the framework of global health. 2. Understand the process of forming a synthesis from literature review, quality assessment, statistical analysis, to manuscript writing. 3. Understand the statistical theory and the application of different statistical theories of meta-analysis. 4. Understand the management and assessment of quality of care for health data. 5. Report the results from their personal project properly. College of Public Health Downtown Campus-College of Public Health 1. Students should have the basic concept of epidemiology and biostatistics. 2. Students should have the basic concept of systematic review and meta-analysis. 3. Active participation in class discussion and practical session. Hon-Yen Wu 20 Tuesday 8,9 EPM7007 (849EM0910) 1 (College of Public Health) Graduate Institute of Epidemiology and Preventive Medicine http://epm.ntu.edu.tw/?locale=en