Data is at the center of the so-called fourth paradigm of scientific research that will spawn new sciences useful to the society. Data is also the new and extremely strong driving force behind many present-day applications, such as smart city, manufacturing informatics, and societal security, to name a few. It is thus imperative that our students know how to handle data, analyze data, use data and draw insights from data. This course aims at acquainting the students with the analytical foundation of data handling techniques. The course consists of a series of seminar talks with substantial student participation, in the form of research and presentation in response to posted questions about main topics in data analytics and modeling. 1. Scope Broad topics covered in the course include: •Regression & curve fitting •Probability distribution & parameter estimation •Mixture models, latent variable models & hybrid distributions •Hidden Markov models, Markov random fields, & graphic models •Pattern recognition & decision theory •Neural networks and deep learning Well spend 2-3 weeks on each topic (some may take up to 4 weeks). 2. Format For each topic, a number of questions to help students learn the subject will be posted in advance. Individual student will be assigned to conduct research, answer specific questions and return with presentations to the class. Each student presentation is of duration ~20 min, followed by ~10 min questions and discussion. Students who are assigned to address specific questions have one week time to prepare for the presentation. Common questions shared by all topics are: – What are the problems that gave rise to the particular topic & concept? (The original motivation) – What problems beyond the original motivation will the topic and the related techniques be able to solve? (New and novel applications) – What are the problem formulations with relevant assumptions that have been proposed? (The methodology and formulation) – What are the ensemble of techniques that were developed to solve the problem? (The tools and capabilities) – How do these techniques solve the problem or contribute to the solutions? (The solution mechanism) – What are the limitations of the solutions proposed so far? Any remaining open problems in the topic? (Research opportunities) In addition to these common questions, some topic-specific questions may also be posted and addressed in student presentations. After all posted questions about a subject are addressed in student presentations, one or two commentary sessions by the lecturer on the subject will follow so as to complete the systematic development of understanding of the subject. The course will be primarily conducted in English. To reflect the applicability of the subject matter to local problems, local languages may also be used as the circumstance calls for it. No official textbook is assigned in this course. Students are expected to conduct research with all university provided resources (e.g., books in the library) and information available on the web. Class notes by the lecturer will be distributed in due course. 3. Prerequisite Both graduate and undergraduate students can enroll in the class, as long as they have completed engineering mathematics courses, particularly Probability and Statistics or the equivalent. Overall, students will be exposed to data analytic topics and their historical perspectives, learn to ask and analyze related problems, understand the modeling techniques and their origins, and conceive of new applications and research opportunities. College of Electrical Engineering & Computer Science No written test will be given in the special course. Student presentations are evaluated by the class and moderated by the lecturer. JUANG BIING-HWANG Thursday 234 CSIE5610 3

Data is at the center of the so-called fourth paradigm of scientific research that will spawn new sciences useful to the society. Data is also the new and extremely strong driving force behind many present-day applications, such as smart city, manufacturing informatics, and societal security, to name a few. It is thus imperative that our students know how to handle data, analyze data, use data and draw insights from data. This course aims at acquainting the students with the analytical foundation of data handling techniques. The course consists of a series of seminar talks with substantial student participation, in the form of research and presentation in response to posted questions about main topics in data analytics and modeling. 1. Scope Broad topics covered in the course include: •Regression & curve fitting •Probability distribution & parameter estimation •Mixture models, latent variable models & hybrid distributions •Hidden Markov models, Markov random fields, & graphic models •Pattern recognition & decision theory •Neural networks and deep learning Well spend 2-3 weeks on each topic (some may take up to 4 weeks). 2. Format For each topic, a number of questions to help students learn the subject will be posted in advance. Individual student will be assigned to conduct research, answer specific questions and return with presentations to the class. Each student presentation is of duration ~20 min, followed by ~10 min questions and discussion. Students who are assigned to address specific questions have one week time to prepare for the presentation. Common questions shared by all topics are: – What are the problems that gave rise to the particular topic & concept? (The original motivation) – What problems beyond the original motivation will the topic and the related techniques be able to solve? (New and novel applications) – What are the problem formulations with relevant assumptions that have been proposed? (The methodology and formulation) – What are the ensemble of techniques that were developed to solve the problem? (The tools and capabilities) – How do these techniques solve the problem or contribute to the solutions? (The solution mechanism) – What are the limitations of the solutions proposed so far? Any remaining open problems in the topic? (Research opportunities) In addition to these common questions, some topic-specific questions may also be posted and addressed in student presentations. After all posted questions about a subject are addressed in student presentations, one or two commentary sessions by the lecturer on the subject will follow so as to complete the systematic development of understanding of the subject. The course will be primarily conducted in English. To reflect the applicability of the subject matter to local problems, local languages may also be used as the circumstance calls for it. No official textbook is assigned in this course. Students are expected to conduct research with all university provided resources (e.g., books in the library) and information available on the web. Class notes by the lecturer will be distributed in due course. 3. Prerequisite Both graduate and undergraduate students can enroll in the class, as long as they have completed engineering mathematics courses, particularly Probability and Statistics or the equivalent. Overall, students will be exposed to data analytic topics and their historical perspectives, learn to ask and analyze related problems, understand the modeling techniques and their origins, and conceive of new applications and research opportunities. College of Electrical Engineering & Computer Science No written test will be given in the special course. Student presentations are evaluated by the class and moderated by the lecturer. JUANG BIING-HWANG Thursday 234 CSIE5610 3

1. Introduction of Queueing Model and Review of Markov Chain 2. Simple Markovian Birth and Death Queueing Models (M/M/1, etc) 3. Advanced Markovian Queueing Models 4. Jackson Queueing Networks 5. Models with General Arrival or Service Pattern (M/G/1, G/M/1) 6. Discrete-Time Queues and Applications in Networking To provide the basic knowledge in queueing models and the analysis capability of the queueing models in telecommunications, computers, and industrial engineering College of Electrical Engineering & Computer Science Midterm 45% Final Exam 45% Homework (including programming and simulations) 10% ZSEHONG TSAI Wednesday 789 EE5039 3

1. Review of Random Variables (Papoulis, Chaps. 1-7, and class note) 2. Introduction to Random Processes: General Concepts and Spectral Analysis (Papoulis, Chap. 9, and class note) 3. Gaussian Random Vectors and Gaussian Random Processes (Larson & Shubert, class note) 4. Signal Representation — Karhunen-Love Expansion (Papoulis, Chap. 11, and class note) 5. Narrowband Processes and Bandpass Systems (Davenport and Root, and class note) 6. Poisson Processes (Larson & Shubert, Leon-Garcia, and class note) 7. Markov Processes and Markov Chains (Larson & Shubert, Leon-Garcia, and class note) 8. Queuing Systems (Leon-Garcia) 9. Random Walk Processes and Brownian Motion Processes (Leon-Garcia) The purpose of this course is to provide students with a solid and pertinent mathematical background for thoroughly understanding digital communications and communication networks. It is a prerequisite for advanced study of numerous communication applications, including wireless communications, mobile communications, communication networks, spread spectrum communications, satellite communications, optical communications, radar and sonar signal processing, signal synchronization, etc. The students majoring in communications and networks are strongly recommended to take this course. The course consists of lectures organized in class notes. College of Electrical Engineering & Computer Science Prerequisite: Probability and Statistics. Grading Policy: There will be six homeworks, one every three weeks, one midterm exam, and one final exam. The grading policy is “Homeworks: 30%; Midterm: 35%; Final: 35%”. CHAR-DIR CHUNG Friday 789 EE5041 3

Logic synthesis is an automated process of generating logic circuits satisfying certain Boolean constraints and/or transforming logic circuits with respect to optimization objectives. It is an essential step in the design automation of VLSI systems and is crucial in extending the scalability of formal verification tools. This course introduces classic logic synthesis problems and solutions as well as some recent developments. This course is intended to introduce Boolean algebra, Boolean function representation and manipulation, logic circuit optimization, circuit timing analysis, formal verification, and other topics. The students may learn useful Boolean reasoning techniques for various applications even beyond logic synthesis. College of Electrical Engineering & Computer Science The prerequisite is the undergrad “Logic Design” course. Knowledge about data structures and programming would be helpful. JIE-HONG JIANG Friday 234 EEE5028 3

[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 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

This course will present an introduction to process dynamics and control. Students will learn how to construct dynamic models of process systems, how to analyze process dynamics using Laplace transforms and transfer functions, the characteristic responses of dynamic processes, and the design and implementation of feedback control. Students will also learn to use computer software to model process dynamics and control. By the end of the semester, students should be able to:

1. Construct dynamic models of chemical processes

2. Solve differential equations using Laplace transforms.

3. Build and analyze transfer function and state-space models

4. Understand the dynamic response of representative processes

5. Develop empirical dynamic process models

6. Implement and tune PID controllers

7. Use frequency response methods to analyze processes and design controllers.

8. Understand and implement Feed-forward, ratio, cascade and multi-variable control.
College of Engineering Main Campus Jeffrey Daniel Ward 30 Tuesday 6,7 Wednesday 7 ChemE7011 (524EM1340) 3 (College of Engineering) Graduate Institute of Chemical Engineering http://www.che.ntu.edu.tw/che/?lang=en

PRELIMINARIES
Fundamentals of real variables
Mathematical preliminaries
Fundamentals of uncertainty analysis
Fundamentals of random processes

MARTINGALES, STOPPING TIMES AND FILTRATIONS
Stochastic processes and sigma fields
Stopping times
Continuous time martingales
Reynolds transport theorem
Conservation of dissolved constituent mass

BROWNIAN MOTION
Brownian motion
Markov property
The brownian sample paths

STOCHASTIC INTEGRATION
Construction of the stochastic integral
The change-of-variable formula
Generalized ito rule for brownian motion

STOCHASTIC DIFFERENTIAL EQUATIONS (IF TIME PERMITTED)
Strong solutions
Weak solutions
Approximation methods for uncertainty analysis
Firs-order variance estimation method
Rosenblueth;s probabilistic point estimate method
Harr’s probabilistic point estimate method
Li’s probabilistic point estimate method College of Engineering Main Campus Statistics or Engineering Statistics, Calculus or Engineering Mathematics (I), or approval by the instructor 10 Thursday 3,4,6 CIE7156 (521EM7450) 3 (College of Engineering) Graduate Institute of Civil Engineering, Hydraulic Engineering Division
*Majors-only (including minor and double major students). http://www.ce.ntu.edu.tw/ce_eng/