Information Theory is a senior (undergraduate) level course designed for students who are interested in the quantitative fundamental limits of information. What is information and how to quantify information? What is the ultimate data compression rate and what is the ultimate transmission rate of communication? In this course, we introduce the fascinating theory originated from Claude E. Shannon, which addresses the above fundamental questions in communication theory. We will develop methods and coding techniques to achieve these fundamental limits. Finally, we will also demonstrate the application of information theory to other fields, including statistics (hypothesis testing and estimation) and statistical inferences. 1. Introduce basic topics in information theory, including measures of information, source coding theorem, channel coding theorem, and source-channel separation. 2. Develop methods and coding techniques to achieve these fundamental limits. 3. Show applications of information theory beyond communications, especially in high dimensional statistics and statistical inferences. College of Electrical Engineering & Computer Science Prerequisite: Probability, Linear Algebra, Optional: Random Processes, Communication Systems Homework (30%), Midterm (30%), Final (40%) I-HSIANG WANG Wednesday 234 EE5028 3

1. Overviews of wireless communication systems 2. Modular communication systems and protocol design 3. Eexperiment and algorithm development in IEEE 802.15.4 platform 4. Network and MAC protocol designs for personal and local area networks 5. Mathematical modeling for communication systems and protocols 4. Physical and MAC protocol designs for mobile and wide area networks 7. Cross layer design and optimization for emerging wireless communication systems This course aims at in-depth discussion of wireless communication systems and their protocols. We will focus on the design rationales of communication protocols, the overall network architectures and performance evaluation of complicated wireless systems so that students will be capable of designing next-generation communications systems through rigorous simulation and mathematical analysis. In addition, we will for the first time introduce the IEEE 802.15.4 experiment test bed for hands-on experiments. Studets will learn from the real hands-on experiment the design of wireless protocols and thus to develop new applications in wireless networking. College of Electrical Engineering & Computer Science 1. Probability and Statistics 2. Introduction to Computer Networks 3. C/C++ programming CHUNI-TING CHOU Tuesday 234 CommE5039 3

This course will teach how to design digital technologies that bring people joy, rather than frustration. This course will cover the followings: Techniques for rapidly prototyping and evaluating multiple interface alternatives. Conduct fieldwork with people to help you get design ideas. Make paper prototypes and low-fidelity mock-ups that are interactive and use these designs to get feedback from other stakeholders like teammates, clients, and users. Principles of perception and cognition that inform effective interaction design. Perform and analyze controlled experiments online. Principles and methods to create excellent interfaces with any technology. College of Electrical Engineering & Computer Science HAO-HUA CHU Monday 789 CSIE5641 3

Overview (2 weeks) Application Layer (3 weeks) Transport Layer (3 weeks) Network Layer (3 weeks) Link Layer (1.5 weeks) Mobile and Wireless Networking (1.5 weeks) Multimedia Networking (2 weeks) o. Overviewing the existence and the components of the Internet o. Examining the mechanisms running in various components o. Understanding the nature of the problems these mechanisms are trying to solve o. Programming with Unix-based sockets College of Electrical Engineering & Computer Science 1.Grading Midterm exam 20%, Final exam 20%, Homework assignment 40%, Quiz 15%, Participation 5% 2.Prerequisite Introduction to Computer Programming (required) Introduction to Computers (required) Data Structure and Programming Language (preferred) POLLY HUANG Wednesday 6 Thursday 34 EE4020 3

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/

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/

I open several related courses. Please visit our lab website for more detailed info on how to choose my lectures based on your preference.
http://homepage.ntu.edu.tw/~tksmiki/for_Students_%28zhong_wen%29.html This is a course intended for students with basic knowledge of ecology, statistics, differential equations, and computer programming techniques and had some experience on modeling. We will discuss the application of mathematical modeling and computer programming techniques to investigate ecological questions. We will also discuss statistical analyses for identifying ecological patterns. Students will select a subject base on his/her own interest and present the progress of the chosen topic. The class is mainly in the form of discussion. The objectives are to provide students opportunities to discuss the application of mathematical modeling and computer programming techniques to investigate ecological questions. College of Science Main Campus Students are required to do oral presentation on a topic of ecological modeling and participate discussion. Takeshi Miki 25 Tuesday 8,9 Ocean7153 (241EM3640) 2 (College of Science) Graduate Institute of Oceanography, Marine Biology & Fisheries Division http://www.oc.ntu.edu.tw/?lang=en