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 TO LIGHTING 2. COLOR SCIENCE 3. INRODUCTION TO DIODES 4. CARRIER RECEOMBINATION 5. LED MATERIAL AND DEVICE 6. HIGH POWER LEDS 7. APPLICATION OF LEDS VARIOUS PROGRAM NONE! UNDERGRADUATE STUDENTS ARE EXTREMELY WELCOME TO TAKE THE COURSE! JIAN JANG HUANG Tuesday 789 OE5040 3

EACH LECTURE WILL BE TAILORED ACCORDING TO STUDENTS UNDERSTANDING. SUBJECTS TO BE COVERED FOR THIS COURSE ARE AS FOLLOWS: 1) THEORETICAL REVIEW OF ELECTROMAGNETISM 2) INTRODUCTION TO VARIOUS OPTICAL SIMULATION TECHNIQUES 3) MONTE CARLO TECHNIQUE 4) NUMERICAL SOLUTIONS OF MAXWELL’S EQUATIONS 5) APPLICATION OF THE TAYLOR’S EXPANSION 6) SCALAR WAVE EQUATION 7) THE FINITE-DIFFERENCE TIME-DOMAIN TECHNIQUE 8) PRAGMATIC SIMULATION OF OPTICAL PROBLEMS College of Electrical Engineering & Computer Science PREREQUISITES: – GENERAL PHYSICS – CALCULUS – ELECTROMAGNETISM – BASIC PROGRAMMING SKILLS (MATLAB, FORTRAN, OR C/C++) GRADING FACTORS: ASSIGNMENTS: 35% MIDTERM EXAM: 25% FINAL EXAM: 30% PARTICIPATION IN CLASS : 10% GRADING FACTORS INCLUDE AN ASSESSMENT OF STUDENTS’ UNDERSTANDING OF THE COURSE CONTENT, PARTICIPATION IN CLASS, AND THEIR ABILITY IN COMPLETING THE ASSIGNMENTS. SIMULATION ASSIGNMENTS ARE DESIGNED TO PREPARE STUDENTS WITH HANDS-ON EXPERIENCE OF LIGHT PROPAGATION SIMULATION. STUDENTS ARE EXPECTED TO BECOME FAMILIAR WITH MATLAB. MIDTERM AND FINAL EXAMS WILL SERVE THE PURPOSE TO EVALUATE STUDENTS’ LEARNING PROGRESS. GRADES THUS ARE GIVEN BASED UPON STUDENTS’ ABILITY IN CARRYING OUT THE ASSIGNMENTS AND THEIR PERFORMANCE IN THE MIDTERM AND FINAL EXAMS. Wednesday 789 OE5047 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

THIS COURSE IS OFFERED FOR ELECTRICAL ENGINEERING DEPARTMENT AND GRADUATE INSTITUTE OF ELECTRONICS ENGINEERING.

THIS COURSE IS GRADUATE-LEVEL AND SUITABLE FOR SENIOR UNDERGRADUATE STUDENTS AND GRADUATE STUDENTS.

IT IS A SELECTIVE COURSE AND CONTAINS 13 CHAPTERS.

1: BASIC MOS DEVICE PHYSICSCHAPTER

2: SINGLE-STAGE AMPLIFIERSCHAPTER

3: DIFFERENTIAL AMPLIFIERSCHAPTER

4: PASSIVE AND ACTIVE CURRENT MIRRORS CHAPTER

5: FREQUENCY RESPONSE OF AMPLIFIERSCHAPTER

6: NOISECHAPTER

7: FEEDBACKCHAPTER

8: OPERATIONAL AMPLIFIERSCHAPTER

9: STABILITY AND FREQUENCY COMPENSATIONCHAPTER

10: SWITCH-CAP CIRCUITSCHAPTER

11: BANDAGE REFERENCESCHAPTER

12: NONLINEARITY AND MISMATCHCHAPTER

13: LAYOUT AND PACKAGING
College of Electrical Engineering & Computer Science STUDENTS ARE EXPECTED TO KNOW SOME FUNDAMENTALS OF CIRCUITS AND ELECTRONIC CIRCUITS. GRADING 1. 20% HOMEWORK+ 30% MIDTERM+ 30% FINAL+ 20% PROJECT JRI LEE Friday 789 EE5112 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

THE COURSE IS DESIGNED FOR STUDENTS WHO LIKE TO LEARN ABOUT THE FIELD OF SUSTAINABLE HEALTH AND ENVIRONMENT FROM AN EAST ASIAN PERSPECTIVE IN A GLOBALIZED WORLD. STUDENTS WILL LEARN FACTS AND DEVELOPMENTS IN ISSUES RELATED TO SUSTAINABLE HEALTH AND ENVIRONMENT THROUGH CROSS-COUNTRY LECTURES, MULTIMEDIA VIEWING, PANEL DISCUSSING, AND GROUP PROJECTS AND PRESENTATIONS. THE SCIENCES OF SUSTAINABLE HEALTH AND ENVIRONMENT COVER BROAD AND INTERSECTED DISCIPLINES FROM HEALTH SCIENCES, PHYSICAL SCIENCES TO SOCIAL SCIENCES LOCALLY, REGIONALLY, AND GLOBALLY. STUDENTS’ VIEWS OF SUSTAINABLE HEALTH AND ENVIRONMENT WILL BE CULTIVATED FROM CURRENT AND HISTORICAL PERSPECTIVES AS WELL AS LOCAL AND REGIONAL LIVING EXPERIENCE. GLOBAL PERSPECTIVES OF STUDENTS WILL BE FURTHER CULTIVATED THROUGH IN-CLASS DISCUSSION AMONG STUDENTS, GROUP PROJECTS BY CROSS-COUNTRY TEAMS, AND ESSAY WRITING. GUEST LECTURES BY DISTINGUISHED EXPERTS IN THE FIELDS OF SUSTAINABLE HEALTH AND ENVIRONMENTAL SCIENCES WILL PROVIDE STUDENTS WITH GLOBAL PERSPECTIVES ON SUSTAINABLE ISSUES. College of Public Health CHANG-CHUAN CHAN Wednesday 234 OMIH5076 3 The upper limit of the number of non-majors: 6.

The module will be delivered over one semester, as a blend of lectures, practical exercises, presentation and in-class discussion of reading tasks. Most sessions comprises lectures and practical exercises. The free statistical software R will be used for practical sessions. We aim to make the students learn the basic concepts of statistics and are able to apply the methods and models into practical projects. Students will learn how to perform the analysis by using the R programming language. College of Public Health Active participations in the class discussion and practical sessions are requirements for all students. LU,TZU PIN Thursday 678 EPM8001 3

1. Circuit Variables and Laws (1.4, 1.5) 2. Properties of Resistive Circuits (2.3, 2.4, 2.5) 3. Applications of Resistive Circuits (3.2) 4. Systematic Analysis Methods (4.1, 4.2, 4.3) 5. Dynamic Circuit (5.3) 6. Transient response (9.1, 9.2, 9.3, 9.4) 7. AC Circuits (6.1, 6.2, 6.3) 8. AC Power (7.1, 7.2) 9. Frequency Response and Filters (11.1, 11.2, 11.4) 10. Laplace Transform Analysis (13.1, 13.2, 13.3) 11. Two-Port Networks (14.1, 14.2, 14.3) Understand fundamental knowledge of DC and AC circuits. College of Electrical Engineering & Computer Science PREREQUISITE: 1. CALCULUS 2. PHYSICS (GENERAL PHYSICS) GRADING: 1. QUIZ: 60% (4 Quizzes? choose 3 out of 4) QUIZ #1: DC circuits and analysis CHAPTER 1,2,3,4 QUIZ #2: time-domain analysis CHAPTER 5,9 QUIZ #3: AC analysis CHAPTER 6,7 QUIZ #4: frequency-domain analysis CHAPTER 11 2. FINAL EXAM: 40% CHAPTER 1,2,3,4,5,6,7,9,11,13,14 I-CHUN CHENG Wednesday 2 Friday 34 EE2004 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

This course will cover the following topics: 1. Introduction 2. Antenna Basics 3. The Antenna Family 4. Point Source 5. Array of Point Sources (Phase distribution) 6. Array of Point Sources (Amplitude distribution) 7. Fourier Transform Relation 8. Thin Linear Antennas 9. Loop Antennas 10. End-Fire Antennas 11. Slot and Patch Antennas Students taking this course will have in-depth understanding of the operational principles and basic parameters of antennas, the basic theory, design, and analysis of antenna arrays, antenna measurements, and the radiation mechanisms and performance of commonly-used antenna types. College of Electrical Engineering & Computer Science Prerequisite courses: electromagnetics CHEN SHI-YUAN Friday 234 EE5010 3

SEMINARS IN ENVIRONMENTAL AND OCCUPATIONAL MEDICINE (I) The students will understand study design in environmental and occupational epidemiology College of Public Health The students are required to report their study design and preliminary results Thursday 12 OMIH7055 2