paper reading College of Management LING-CHIEH KUNG IM7093 2

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

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

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

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

This course is based on lectures and discussions on recent research in the field of stem cells. Each week, a faculty member or a student presents a discussion of one or a few related articles in the assigned topics. The article(s) should be deal with research in stem cell and regenerative medicine College of Life Science 1. Contact the course coordinator to enroll in the class. 2. All participants present at least twice. 3. Review the assigned articles before attending the class. 4. Participate in and contribute to class discussions. SU-YI TSAI LS7036 2

This course is suitable for student who is interested in Developmental Biology (DB), but have no expereince in reading and presenting a scientific paper. An introduction of how a scientific paper is organized will be given at the beginning of course. General approaches and methodologies frequently used by a developmental biologist will be covered in the course. Later on, we will actively dissect several hypothesis-driven DB papers. Students will be required to transform a published paper into a scientific proposal and then based on the proposal to rewrite a manuscript. Each student is required to actively participate in our discussion and present papers in English. College of Life Science Rewrite a manuscript Prepare a proposal Presenring a paper SHYH JYE LEE LS7028 1

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

1. INTRODUCTION-FROM GENE TO GENETICS 2. ONLINE RESOURCES FOR GENETICS 3. FUNCTIONAL EXPRESSION OF A GENE, TRANSCRIPTIONAL REGULATION AND FUNCTIONAL REGULATION 4. TECHNIQUES FOR PHYSIOLOGICAL GENETICS RESEARCH 5. GENES INVOLVED IN ANIMAL GROWTH-MOLECULAR REGULATION OF MUSCLE GROWTH 6. POLYMORPHISM AND SINGLE NUCLEOTIDE POLYMORPHISM _ TECHNIQUES AND APPLICATION 7. GENETIC POLYMORPHISM AND BODY COMPOSITION 8. GROWTH SELECTION AND GENE EXPRESSION 9. MIDTERM 10. FUNCTIONAL EXISTENCE OF ADIPOSE TISSUE AND ADIPOKINES 11. MOLECULAR REGULATION OF ADIPOCYTE DIFFERENTIATION AND MOLECULAR MARKERS FOR OBESITY 12. NUTRITIONAL GENOMICS RESEARCH PROGRESSES 13. PROTEOMICS RESEARCH APPROACHES FOR SEARCHING SECRETIVE PROTEINS 14. PRODUCTION OF MONOCLONAL ANTIBODY BY TRANSGENIC ANIMALS 15. GENOME-WIDE KNOCKOUTS 16. FINAL REPORT 1 17. FINAL REPORT 2 18. FINAL REPORT 3 College of Medicine TANG-LONG SHEN Wednesday 234 Prog5106 3