THE OBJECTIVES OF THIS COURSE ARE

1) TO PROVIDE GRADUATE STUDENTS WITH AN OVERVIEW OF RECENT DEVELOPMENTS IN ANIMAL BIOTECHNOLOGY;

2) TO IMPROVE GRADUATE STUDENTS’ PRESENTATION SKILLS. AFTER EXTENSIVE REVIEW AND DISCUSSION OF VARIOUS BIOTECHNOLOGIES, EACH STUDENT WILL BE ASKED TO GIVE A PRESENTATION IN THE AREA OTHER THAN THEIR OWN RESEARCH.

I WILL MEET WITH ALL STUDENTS INDIVIDUALLY SEVERAL TIMES DURING THEIR LITERATURE SEARCH, PREPARATION OF PRESENTATION OUTLINE AND PRESENTATION PRACTICE.

LAB DEMONSTRATIONS OF BASIC EMBRYOLOGY TECHNIQUES WILL BE INCLUDED AS WELL. GUEST SPEAKERS WILL PLAN TO INVITE FOR THE LECTURES AS NECESSARY. College of Bio-Resources & Agriculture Wednesday 234 Biot5007 3

This course introduces the state-of-the-art energy technology and development. Subjects include energy generation, storage and conversion technology and related applications will be covered. For example: hydrogen economy, nuclear energy, wind power and solar cells, batteries, green buildings etc. I aim to prepare students with abilities of active learning and creative thinking. Innovative pedagogical methods such as fishbowl discussion, brainstorming, mock conference, and debate etc. will be practiced in this class. College of Bio-Resources & Agriculture Students are required to study the assigned contents each week, and exchange ideas and thoughts in class. 50% of the final grade is based on in-class discussions, while the other 50% is based on the final report. The topics of final projects, which focus on energy issues facing Taiwan, will be developed over the course of the classes by each student. HSUN-YI CHEN Tuesday 6 Friday 34 BME5920 3

INTRODUCTION?EPIGENETIC OVERVIEW DNA METHYLATION AND GENOME DEFENSE RNAI AND HETEROCHROMATIN EVOLUTION OF MAMMALIAN EPIGENETIC CONTROL SYSTEMS EPIGENETICS AND DEVELOPMENT EPIGENTICS AND HUMAN DISEASE X-INACTIVATION GENOMIC IMPRINTING IN MAMMALS GENOMIC IMPRINTING IN PLANTS EPIGNETICS AND REPROGRAMMING EPIGENETICS IN ASSISTED REPRODUCTIVE TECHNIQUES APPLIED EPIGENETICS: FLOWERING PLANTS AND TISSUE ENGINEERING College of Bio-Resources & Agriculture Monday 678 Biot8001 3 The upper limit of the number of non-majors: 15.

This is a class integrates the concepts of structural biology and bioinformatics. The basic principle of amino acids and structre will be explained first, then the modern methods to resolve atomic resolution of protein structures. The relation of protein structure and function will be emphasized in the third part of this class. Bioinformatic principles, methods and modern developments will be followed. 1. Understand amino acids and protein structures. 2. Modern approaches to resolve protein structure. 3. Protein structure and function. 4. Software-based analysis of protein sequence. 5. Bioinformatic theories 6. Current developments of bioinformatics methods. College of Life Science This class will be taught in English. CHII-SHEN YANG Friday 678 Biot8003 3 The upper limit of the number of non-majors: 15.

This course includes presenters of Taiwan, Japan, Thailand, and Brunei, to provide understanding of international perspectives of environmental and occupational health. For the students to understand international perspectives of environmental and occupational health, and to interact with international teachers and students DISTANCE LEARNING NATIONAL TAIWAN UNIVERSITY Interaction with international teachers and students final report and presentation Tuesday 67 OMIH5056 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

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

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. 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

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

In this class, I will cover the basic techniques for design and analysis of algorithms. I will also give a brief introduction to advanced topics such as approximate algorithms and randomized algorithms. 1 Introduce different algorithm design techniques. 2 Teach the students how to evaluate the performance of different algorithms. College of Electrical Engineering & Computer Science Grading: Homework: 40% Midterm: 30% Final exam: 30% HO-LIN CHEN Tuesday 234 EE5048 3