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2017-08-19 14:43:54

课程安排

何毓琦教授在 ISINS'04
学期课程
短期课程

[2016.03.21] 云计算(Cloud Computing)

【课程编号】:Y0250431

【上课时间】:2016年3月21、23、25、28、30日,下午14:00-16:00

【上课地点】:中央主楼511

【主讲教师】:Timothy Chou博士

【授课对象】:研究生、高年级本科生

【学分及考核方式】:1学分,考查

【课程简介】:课程内容涵盖云计算的基本概念、运行优化和基于云计算的商业模式。课程特色是紧密软件行业的发展最新趋势,介绍如何充分发挥云计算技术的优势。对我校信息学院、经管学院等相关院系的研究生知识体系是很好的补充。 Based on his Stanford cs309a.stanford.edu class, numerous keynote speeches and enterprise workshops, Dr. Timothy Chou has developed 24 TED-sized lectures to first introduce you to the basics of cloud computing, next how to use the technologies to achieve operational efficiency, and finally how cloud computing can transform business. These will be delivered over 2 weeks (MWF) – 2 hours of lecture per day.

【主讲人简历】

TimothyChou博士毕业于美国伊利诺伊大学,是美国上市公司Blackbaud的董事。他也担任美国斯坦福大学的兼职教师负责斯坦福大学通过edx平台免费提供的云计算课程(Stanford University’s CS309A)。

[2015.05.20] 离散事件、混杂系统与物联网(Discrete Event, Hybrid Systems, and the Internet of Things)

【课程编号】:Y0250401

【上课时间】:2015年5月21、22、25、26日,下午13:30~16:45

【上课地点】:六教6A005

【主讲教师】:Christos G. Cassandras教授,美国波士顿大学教授,IEEE Fellow

【授课对象】:研究生、高年级本科生

【学分及考核方式】:1学分,考查

【课程简介】:本门课程面向研究生及高年级本科生,讲授离散事件系统、混杂系统和同时具有连续(时间驱动)和离散(事件驱动)的动态过程的信息物理融合系统。课程强调对这类系统进行控制与优化所需的数据驱动的方法,强调大数据对这些系统分析方法的影响,并介绍物联网。

【教学大纲】

1. Modeling frameworks for Discrete Event Systems

2. Modeling frameworks for Hybrid Systems

3. Discrete Event and Hybrid computer simulation

4. Intelligent simulation and data-driven Rapid Learning methods

5. Applications to Cyber Physical Systems and Internet of Things

【主讲人简历】

Christos G. Cassandras is Distinguished Professor of Engineering at Boston University. He is Head of the Division of Systems Engineering, Professor of Electrical and Computer Engineering, and co-founder of Boston University’s Center for Information and Systems Engineering (CISE). He received degrees from Yale University (B.S., 1977), Stanford University (M.S.E.E., 1978), and Harvard University (S.M., 1979; Ph.D., 1982). In 1982-84 he was with ITP Boston, Inc. where he worked on the design of automated manufacturing systems. In 1984-1996 he was a faculty member at the Department of Electrical and Computer Engineering, University of Massachusetts/Amherst. He specializes in the areas of discrete event and hybrid systems, cooperative control, stochastic optimization, and computer simulation, with applications to computer and sensor networks, manufacturing systems, and transportation systems. He has published over 350 refereed papers in these areas, and five books. He has guest-edited several technical journal issues and serves on several journal Editorial Boards. In addition to his academic activities, he has worked extensively with industrial organizations on various systems integration projects and the development of decision-support software. He has most recently collaborated with The MathWorks, Inc. in the development of the discrete event and hybrid system simulator SimEvents®.

Dr. Cassandras was Editor-in-Chief of the IEEE Transactions on Automatic Control from 1998 through 2009 and has also served as Editor for Technical Notes and Correspondence and Associate Editor. He was the 2012 President of the IEEE Control Systems Society (CSS). He has also served as Vice President for Publications and on the Board of Governors of the CSS, as well as on several IEEE committees, and has chaired several conferences. He has been a plenary/keynote speaker at numerous international conferences, including the American Control Conference in 2001 and the IEEE Conference on Decision and Control in 2002, and has also been an IEEE Distinguished Lecturer.

He is the recipient of several awards, including the 2011 IEEE Control Systems Technology Award, the Distinguished Member Award of the IEEE Control Systems Society (2006), the 1999 Harold Chestnut Prize (IFAC Best Control Engineering Textbook) for Discrete Event Systems: Modeling and Performance Analysis, a 2011 prize and a 2014 prize for the IBM/IEEE Smarter Planet Challenge competition (for a “Smart Parking” system and for the analytical engine of the Street Bump system respectively), the 2014 Engineering Distinguished Scholar Award at Boston University, several honorary professorships, a 1991 Lilly Fellowship and a 2012 Kern Fellowship. He is a member of Phi Beta Kappa and Tau Beta Pi. He is also a Fellow of the IEEE and a Fellow of the IFAC.

[2014.05.30] 近似动态规划(Approximate Dynamic Programming)

【课程编号】:Y0250361

【上课时间】:2014年6月9、11、13、16、18、20日,下午14:00~16:00

【上课地点】:除6月13日在FIT-1-315,其余均在FIT-1-312

【主讲教师】:Dimitri P. Bertsekas教授,美国工程院院士

【授课对象】:研究生、高年级本科生

【学分及考核方式】:1学分,考查

【课程简介】:基于近似和仿真的大规模动态规划在过去二十多年时间里成为活跃的研究领域,有着强化学习、神经元动态规划等不同的名字,并且汲取了人工智能、优化、控制等领域的大量成果。本门课程主要讲授在不确定性下对动态系统的控制问题,但有更广的适用范围,比如离散确定性优化问题。相关理论在控制理论、运筹学、人工智能等领域有广泛的应用。将展示如何通过近似与仿真来解决动态规划的两个基本困难——维数灾与建模。

【主要教材】

Dimitri P. Bertsekas, Dynamic Programming and Optimal Control: Approximate Dynamic Programming, Athena Scientific, 2012. http://www.athenasc.com/dpbook.html

【教学大纲】

1. 无穷阶段精确动态规划

2. 大规模精确动态规划的计算方法

3. 大规模问题中近似与仿真的一般方法

4. 基于temporal difference、投影、Galerkin近似的近似策略迭代

5. 集结方法

6. 随机逼近、Q-学习及其他方法

【主讲人简历】

Dimitri Bertsekas studied Mechanical and Electrical Engineering at the National Technical University of Athens, Greece, and obtained his Ph.D. in system science from the Massachusetts Institute of Technology. He has held faculty positions with the Engineering-Economic Systems Dept., Stanford University, and the Electrical Engineering Dept. of the University of Illinois, Urbana. Since 1979 he has been teaching at the Electrical Engineering and Computer Science Department of the Massachusetts Institute of Technology (M.I.T.), where he is currently McAfee Professor of Engineering.

His research spans several fields, including optimization, control, large-scale computation, and data communication networks, and is closely tied to his teaching and book authoring activities. He has written numerous research papers, and fourteen books, several of which are used as textbooks in MIT classes. His involvement with dynamic programming started with his Ph.D. thesis research, and has continued through the present with many research papers, and several books and research monographs.

Professor Bertsekas was awarded the INFORMS 1997 Prize for Research Excellence in the Interface Between Operations Research and Computer Science for his book “Neuro-Dynamic Programming” (co-authored with John Tsitsiklis), the 2000 Greek National Award for Operations Research, the 2001 ACC John R. Ragazzini Education Award, and the 2009 INFORMS Expository Writing Award. In 2001, he was elected to the United States National Academy of Engineering for “pioneering contributions to fundamental research, practice and education of optimization/control theory, and especially its application to data communication networks.”

[2014.01.6] 马尔可夫链模型的应用(Applications of Markov Chain Model)

【课程编号】:Y0250331

【上课时间】:1.6-1.9(周一二三四),每日上午9:00-12:00

【上课地点】:FIT楼1-312

【主讲教师】:程玮琪(香港大学数学系教授)

【授课对象】:研究生、高年级本科生

【学分及考核方式】:1学分,考查

【课程简介】:该课程介绍隐马尔可夫链及高维马尔可夫链及其在互联网、制造与再制造系统、排队系统、仓储管理,DNA序列、遗传网络以及信用风险模型等方面的应用。该课程的内容对随机控制与优化的多门课程都有很好的补充,参考和借鉴价值,对相关的研究工作也会有启发。

【教学大纲】

第一讲:马尔可夫链:理论、实例及计算 (1月6日上午9:00-12:10)

第二讲:排队系统与制造系统的应用 (1月7日上午9:00-12:10)

第三讲:多元马尔可夫链的应用 (1月8日上午9:00-12:10)

第四讲:隐马尔可夫模型的应用 (1月9日上午9:00-12:10)

【主讲人简历】

程玮琪(Wai-Ki Ching),分别于1991和1994年获香港大学理学士(Horns) 和哲学硕士学位,1998年获香港中文大学哲学博士学位 M. Phil. (1994) 。曾在剑桥大学从事博士后研究及南安普敦大学数学研究所任讲师。现任香港大学数学系副教授。曾获Copper Mountain Conference (Colorado University and SIAM) U.S.A. (1998)最佳学生论文奖、香港中文大学工程学院优秀博士论文(1998,唯一获奖人)、IEEE(香港地区)研究生论文比赛优秀奖(1998)、裘槎基金会奖学金(1999)以及香港大学及海外院校的多种奖项。研究兴趣为随机建模与矩阵计算,特别包括随机模型的应用、与马尔可夫链及其应用相关的数值算法、生物信息学、图像处理、管理科学和数量金融。已出版论文论著包括6本书、5本杂志特刊(编辑)、150多篇学术期刊论文、40多本书的章节以及60多篇会议论文。

[2013.06.19] 多智能体系统的分布式控制

清华大学自动化系邀请美国加州大学河边分校(University of California at Riverside)电气工程系任伟副教授讲授的短期课程《多智能体系统的分布式控制》将于6月19日(周三)开课,1学分,欢迎全校同学选修或旁听。

【课程编号】:

【上课时间】:6.19-6.21(周三四五),6.24-6.25(周一二),每日上午9:00-12:00

【上课地点】:清华大学中央主楼511

【主讲教师】:任伟(Wei Ren,美国加州大学河边分校副教授)

【授课对象】:研究生、高年级本科生

【学分及考核方式】:1学分,考查

【课程简介】: This course will introduce a consensus approach for distributed multi-vehicle cooperative control. While autonomous vehicles that perform solo missions can yield significant benefits, greater efficiency and operational capability will be realized from teams of autonomous vehicles operating in a coordinated fashion. Potential applications for multiple autonomous vehicles include autonomous household appliances, hazardous material handling systems, distributed reconfigurable sensor networks, surveillance and reconnaissance, space-based interferometry, and future autonomous combat systems. To enable these applications, a variety of cooperative control capabilities need to be developed. These capabilities include formation control, rendezvous, attitude alignment, flocking, foraging, task and role assignment, payload transport, air traffic control, and cooperative search. Execution of these capabilities requires that individual vehicles share a consistent view of the objectives and the world. Information consensus guarantees that vehicles sharing information over a network topology have a consistent view of information that is critical to the coordination task. By necessity, consensus algorithms are designed to be distributed, assuming only neighbor-to-neighbor interaction between vehicles. Consensus algorithms have applications in rendezvous, formation control, flocking, attitude alignment, and sensor networks.

【教学大纲】

Objective: The purpose of this course is to overview distributed control algorithms, in particular consensus algorithms and their applications in multi-vehicle cooperative control. Theoretical results on distributed consensus algorithms for multi-agent systems are first introduced. The dynamics of the agents evolve according to first- or second-order dynamics and can be governed by rigid body attitude dynamics and Euler-Lagrange equations. Application examples are then investigated where the distributed consensus algorithms are used for coordinating vehicle formations, such as the rendezvous and formation keeping tasks. The mobile agents under consideration include wheeled mobile robots and deep space spacecraft.

Contents:

1. Overview of recent research on distributed multi-vehicle cooperative control.

2. Consensus algorithms for single and double-integrator dynamics. To be more specific, we introduce the basic consensus algorithms for single-integrator dynamics, consensus tracking of a dynamic leader, consensus algorithms for double-integrator dynamics, consensus under realistic constraints, and swarm tracking algorithms.

3. Consensus algorithms for rigid body attitude dynamics and Euler-Lagrange systems. In particular, we discuss attitude consensus for multiple rigid bodies, reference attitude tracking, and leaderless consensus for networked Lagrangian systems.

4. Applications of distributed consensus algorithms in multi-vehicle cooperative control. Specific topics include consensus-based design methodologies for rendezvous and axial alignment with multiple wheeled mobile robots, distributed formation control of multiple wheeled mobile robots with a virtual leader, and deep space spacecraft formation flying.

Lecture Notes: Will be distributed during the course.

Preliminaries: Basic background in systems and control theory.

【主讲人简历】

Wei Ren received the B.S. degree in electrical engineering from Hohai University, China, in 1997, the M.S. degree in mechatronics from Tongji University, China, in 2000, and the Ph.D. degree in electrical engineering from Brigham Young University, Provo, UT, in 2004. From October 2004 to July 2005, he was a Postdoctoral Research Associate with the Department of Aerospace Engineering, University of Maryland, College Park. He was an assistant professor (August 2005 to June 2010) and an associate professor (July 2010 to June 2011) with the Department of Electrical and Computer Engineering, Utah State University, Logan. Since July 2011, he has been with the Department of Electrical Engineering, University of California, Riverside, where he is currently an Associate Professor. He is an author of two books Distributed Coordination of Multi-agent Networks (Springer-Verlag, 2011) and Distributed Consensus in Multi-vehicle Cooperative Control (Springer-Verlag, 2008). Ren was a recipient of the National Science Foundation CAREER Award in 2008. He is currently an Associate Editor for Systems and Control Letters and an Associate Editor on the IEEE Control Systems Society Conference Editorial Board.

[2009.11.30] Dr. Timothy Chou’博士的短期课程“云计算及软件工业的商业模型”

课程概况:Timothy Chou博士曾任甲骨文公司下属的Oracle On Demand总裁,现任著名软件上市公司Blackbaud的董事。本课程面向研究生和高年级本科生介绍刚刚兴起的新型计算技术-云计算及其商业应用前景。适合于信息学院、经管学院等院系相关专业的学生。Chou博士有在斯坦福大学讲授计算机体系结构15年的教学经验。此次开设的课程他在斯坦福大学已经讲过4次。课程将从技术和商业应用两个方面介绍云计算,其中包含大量的企业应用案例。

【课程编号】:Y0250131

【上课时间】:11月30日至12月9日(周一、三、五下午3:10-5:40)

【上课地点】:中央主楼510教室

【主讲教师】:Timothy Chou (软件上市公司Blackbaud董事,斯坦福大学兼职教员)

【授课对象】:研究生、高年级本科生

【联系人】: 自动化系赵千川 电话:83612,电子邮件zhaoqc@tsinghua.edu.cn

【学分及考核方式】:1学分,考查

【课程简介】: 主讲教师Timothy Chou博士曾任甲骨文公司下属的Oracle On Demand总裁,现任著名软件上市公司Blackbaud的董事。本课程面向研究生和高年级本科生介绍刚刚兴起的新型计算技术-云计算及其商业应用前 景。适合于信息学院、经管学院等院系相关专业的学生。Chou博士有在斯坦福大学讲授计算机体系结构15年的教学经验。此次开设的课程他在斯坦福大学已经 讲过4次。课程将从技术和商业应用两个方面介绍云计算,其中包含大量的企业应用案例。

The objective of this class is for business and technology students to understand 1.The fundamental business models for the software industry. 2.The current state of the art in cloud computing 3.How all of this technology can be used to create new businesses The lecture series will contain numerous examples from my Stanford lecture series and some of the leading technology companies including Cisco, Webex, Concur, Salesforce, Zappos, Opentable, IBM, Oracle, Blackbaud, Microsoft, RightNow, Kenexa, Taleo, Netsuite, Google, eBay, Pandora, Amazon, Rackspace and Opsource.

【教学大纲】

Lecture 1: Seven Software Business Models The initial lecture will focus on the seven fundamental software business models, which included traditional, open source, outsourcing, hybrid, hybrid+, software as a service and Internet. While many have been students of the economics of hardware, few have been students of the economics of software. This lecture will focus on the cost structures both to the producer and the consumer of software.

Lecture 2: Application Cloud Services Almost all software companies that have made public offerings (IPO) since 2000 deliver their applications as a cloud service. We will discuss many of them, how they are similar, how they are different.

Lecture 3: Network and Co-location Cloud Services The fundamental base of the cloud service stack is networking and collocation. We will discuss some of the essentials in developing high quality, high reliability, low cost services for networking and housing the computers and storage.

Lecture 4: Compute and Storage Cloud Services Many of the consumer Internet providers and some of the business service providers are now making lower level infrastructure available to developers to either build totally new applications or extend existing apps. Led by Amazon, many companies are beginning to provide computers and storage as a cloud service. We’ll discuss some of the challenges and solutions in this area.

Lecture 5: Research and Development: Platform Cloud Services Begin with a basic understanding of the economics of software development and then discuss the implications on R&D when developing software for the cloud. Issues to be discussed include the weight of the application, database architecture, implications on test & QA, how to manage the issues of customization and fundamental billing infrastructure. Lecture 6: Operations: Platform Cloud Services Few in the academic setting understand the challenges of managing deployed software. But anybody delivering cloud-based services will quickly realize operations are not an afterthought. In this lecture we will cover the key operational processes: security, availability, performance, change and problem management.

Lecture 7: Marketing Building the greatest cloud service in the world is good ? but if no one knows about it who will care? This lecture will focus on modern social media mechanisms to market products and services. Blogging, search, social networks and how you measure marketing results will all be covered.

Lecture 8: Sales Marketing is important, but ultimately you have to take qualified leads and complete the sale. Depending on the average selling price of the service and the size of the installed base there are many sales models that can be effective. The lecture will focus on some of the key success factors including sales compensation, the usage of indirect channels and the math behind figuring out when to invest more in selling. Remember almost every technology company spends more money on sales and marketing than on R&D.

Lecture 9: Finance and Business Operations The cloud-based business models have a very different revenue model than traditional software. This has implications on revenue recognition, contracts, and service level agreements. An analysis of three companies and the amount of investment required to establish them will be covered.

Lecture 10: Culture and Organization Cloud-based businesses will always be about the people you can hire, motivate and keep. We will discuss what it means to have the right people, right culture and right organization with examples from companies like Zappos.com

【主讲人简历】

Timothy Chou has over twenty years of experience in the technology business. Over the past nine years he has been a visible pioneer of the movement to software delivered as a cloud service. From 1999 to 2005 he was President, Oracle On Demand. Oracle On Demand delivered enterprise applications as a service to over a quarter million people worldwide.

Under his leadership Oracle On Demand was the fastest growing business inside Oracle. In order to sustain this kind of growth many business and technology innovations were developed to speed the adoption by medium and large corporations both in the United States and internationally. Customers included companies such as Cigna, Bank of Montreal, Unocal, Grupo Posadas, Thermos, and Qantas Airlines

Since leaving Oracle in 2005 he helped co-found two new cloud-based ventures. The first venture is using some fundamental game mechanics to change consumer behavior towards a more sustainable lifestyle. The second venture is building the world’s first search (not SQL)-based applications. He also joined the board of Blackbaud (NASDAQ: BLKB) in 2006. Blackbaud is the world’s leading provider of software for nonprofits.

Based on his experiences as President, Oracle On Demand he authored his popular first book, “The End of Software”. Based on the success of that book he has written a follow on book “Cloud: Seven Clear Business Models” which will serve as a tutorial for anyone on both the technology and business aspects of cloud computing.

In addition to his professional career he has also kept his hand in the academic community. For over 15 years he taught the introductory computer architecture class at Stanford University in the Spring and Fall Quarters. He returned to Stanford to launch the first class on software as a service. The class has features CEOs and founders of leading public software companies. These have included the leaders of Sun Microsystems, Webex, Concur, Taleo, Salesforce.com, Netsuite, Google, eBay and Amazon. The upcoming class in the Fall of 2009 will mark the 5th time the class has been given.

Dr. Timothy Chou’博士的短期课程
Dr. Timothy Chou’博士的短期课程

原始大小图片: [1] [2]

[2008.5.20] Meerkov 教授和李京山教授开始短期课程

经校研究生院批准,我中心邀请自动化系申报的海外学者 Meerkov 教授和李京山教授开始的短期课程 - 生产系统工程已经正式开课。选课同学请访问课程链接下载相关文件。

Meerkov 教授和李京山教授短期讲学

主讲人: Semyon M. Meerkov 教授和李京山教授

助教: 张亮

课程名称:生产系统工程(Production Systems Engineering)

课程编号:Y0250062

生产系统工程,研究和制造有关的建模、设计和优化问题,具有广泛的工程背景。包括生产系统的设计,运行管理,供应链与物流管理,半导体制造,电路设计,生产自动化和控制,生产计划与调度等。该课程是根据 Meerkov 教授和李京山博士在美国密西根大学电子工程与计算机科学系和美国肯塔基大学电子与计算机工程系开设的相应课程的内容组织的,面向与制造相关的所有相关工程学科的高年级本科和研究生开设。侧重以随机过程为理论基础,面向生产系统的工程实践,建立分析生产系统性能的模型和方法,为生产系统的设计和优化提供理论依据和指导。由于主讲教师具有丰富的实际工程经验,因而,该课程在重视理论严谨性和系统性的同时,也会紧密结合实际,对理论和方法在实践中的运用技巧给出透彻的解释。

Semyon M. Meerkov教授简历

Semyon M. Meerkov 教授 1966 年从前苏联莫斯科控制科学研究所获得系统科学博士学位。在前苏联莫斯科控制科学研究所从事研究工作至 1977 年。由国际著名的优化专家贝尔曼介绍到美国进行合作研究, 后执教于伊利诺伊理工学院。自 1984 年起在美国一流的密西根大学(University of Michigan, Ann Arbor)电子工程与计算机科学系担任终身教授。曾在美国斯坦福大学和加州大学洛杉矶分校访问。

Meerkov 教授的研究方向是系统与控制理论及在生产系统和通信网络中的应用。由于他在生产系统的研究上的突出成就,曾获 1990 年国际自动控制联合会世界大会的应用论文奖;并当选为 IEEE 会士(Fellow)。他担任着国际知名学术期刊《Mathematical Problems in Engineering》的主编,并任国际著名期刊《IIE Transactions》在制造系统领域的编辑和多家杂志的编委。曾多次在主要国际会议上作大会报告。

李京山(Jingshan Li)教授简历

李京山博士 1989 年本科毕业于清华大学自动化系,1992 从中国科学院自动化研究所获得硕士学位,2000 年从美国密西根大学(University of Michigan, Ann Arbor)获得博士学位,师从 Semyon M. Meerkov 教授。2000 年-2006 年就职于美国通用汽车公司制造系统研究院(美国),从事研究工作。2006 年加入美国肯塔基大学,担任助理教授。

李京山博士的研究方向是生产系统的建模分析和优化。由于他在该方向上做出的突出成绩,曾获得 2005 年度 IEEE 自动化科学与工程汇刊最佳论文奖(惟一获奖人),2006 年度 IEEE 机器人与自动化学会颁发的 Early Industry/Government Career Award in Robotics and Automation。他担任着国际学术期刊 IEEE 自动化科学与工程汇刊和《Mathematical Problems in Engineering》的编委。