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Celebrating JIm Yong Kim
Tsinghua #1 DigitalCooperation MIT #2
18th annual update of hubsworld
happy 2019- 2'oclock rock on stopping extinction of species
Do you know womens most valuable lesson from 20th century?
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AI round the world
50 years mapping why sustainability needs East to be in top 2 all AI markets by 2025
what if AI world's robots were programmed by Trump?
GPLETFORC League of Nations
This months youthcooperation meetings
world record job creators before moon landing
#BR0 young leaders : naturally china largest gdp by 2030 whatever trump does
9* Sir Fazle Abed
China Capitalism - How Women & Youth were asked to Change www sustainability
supernation economics valuetrue maths crisis: nature values geo & eco systems more than humans
How can you help students and teachers who discover sustainability's missing solutions?
Love & Sustainability: NormanMacrae.net remembered by son and grandddaughter
2019 Geneva to honor huawei and top 20 of made in the internet
Listing 100 reasons why big organisations have zero value unless they serve small enterprises
Huawei wizards number 1 brand
#BR2 South Asia's Coastal Belt
EconomistWomen.com - further references
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questions on NY's AI Now Report
where can people chat seriously about youth livelihoods
how to help next 2 years of research of world record jobs
what supercities does a nation need to sustain fifth of world people and worldwide youth
HubWorld Update
Game- choose 5 co-leaders to sustain mother earth
DigitalCooperation- Sustainability Youth World Trade Flight Map
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Sports Arts top 10 non-sustainable market
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#BR1 East of China &
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universe of world record jobs and sustainability
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#BR10 latin america
#BR9
news blockchain & edu belt road
stories developing for new uni alibabauni and arcticuni ...
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LoveQ
Prince Charles
Can Artificials win-win with human Intelligences
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old homepage
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50 shades of green
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?Mapping 3 millennia of world trade
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MICES- Map-Integrate-Community-Economics-Sustainability
How English Failed Democracy's FInal Exam 2 Centuries in a Row
Guide to 4000 times Moore tech era of WorldRecordJobs Creators
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unctad dialogue
Yunus top 10 concepts - what to learn, what not to copy?
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education's 3 greatest jobs creators
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Place branding- how singapore rose to top of class as US. EU bottomed
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The United Nations of Girls
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MIT Matech US-East
Mapping Economics of Ports & Railroads & Digital
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Global Uni of Poverty - why SDgirls value china's BAT not US FAG
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Cost of Girls True News
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Wang Jianlin
BRi.school why under 30s need to value boats & trains more than cars and planes
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Who's Afraid of Diversity of 195 nations
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amy space
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g20 argentina summit july 2018
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100 years of reporting the market for poverty alleviation
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East's 70 years of amazing development economic models
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un year of ecotourism
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100 days -- 1/14.6 #TheEconomist
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#youthtech - worldwide brand charter search by Baltimore
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2020,2019,2018 = now!
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Conscious Post Its
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1* Jack Ma
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business models
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Game 1 Top 10 Open Universities that value youth most?
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Saving Youth - Top 100 Videos to Viralise
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Blank page
20 classes -cataloguing frames of partners in publishing world record job creation
Why nature will not sustain human species unless act now on biggest mistakes economists made Q3 C21
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old grameeneconomics.com
old yunusworld.com
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#BR0 China & #TheEconomist
#BR9 Africa #theeconomist economistafrica.com
#BR3 Russia #BR4 East Europe #TheEconomist
#BR7 Middle East #TheEconomist
#BR11 Arctic Belt Road
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some curricula

 Tsinghua Computer science as at Jan 2019 -faculty

 Machine Learning

The course introduces the advanced theory of machine learning and its related algorithms. The course will first review the state-of-the-art machine learning algorithms and the course’s content mainly consists of probabilistic generative learning and probabilistic discriminative learning. Based on the theoretical analysis and algorithmic application, we plan to introduce the following subtopics:
(1) Probabilistic topic model
(2) Restricted Boltzmann machines
(3) Factor graph model
(4) Bayesian nonparametrics
(5) Semi-supervised learning
(6) Scalable machine learning
The course requires all students to design and implement an algorithm for advanced machine learning, and validate the algorithm on the our provided platform.

Lecturer(s)PrerequisitesGrading
Jie TANG, Jun ZHUData representation(e.g.,vector space model,language model); Basic probability theory(e.g., likelihood,conditional probability,posterior probability,Bayes);Basic linear algerbra(e.g.,linear transformations,eigenvalues,least-squares best fit)

 

  • Distributed Systems

    This is an introductory course on distributed systems. This course introduces the principles of distributed systems as well as some of the current influential large-scale distributed systems such as Google file system, MapReduce, Amazon Dynamo etc. To make the course more concrete, this course uses a series of labs requiring the students to build real distributed systems. This course emphasizes on the general principles of building distributed systems in addition to introducing important practical distributed systems. For example, the various kinds of distributed consistency protocols will be discussed and such principles can be adopted in many kinds of real distributed applications. The current systems used by Googe, Amazon, Microsoft will be introduced.

    Lecturer(s)PrerequisitesGrading
    Kang CHENdata structure, C programmingClass attendance is required, Readings and Homework (20%), Labs (40%), Final Exam (40%). Exam will be closed-book.
    • Distributed Database System

      The aim of the course is to 1) enhance the previous knowledge of database systems by deepening the understanding of the theoretical and practical aspects of the database technologies, and showing the need for distributed database technology to tackle deficiencies of the centralized database systems; 2) introduce basic principles and implementation techniques of distributed databases including distributed database design and architecture, query processing and optimization, transaction management, recovery, and reliability protocols, and to 3) expose active research issues in distributed database systems and application development.

      Lecturer(s)PrerequisitesGrading
      Ling FENGDatabase Management SystemsClass attendance is required, Examination (40%) and Practical project (60%)
      • Fundamentals of Computer Graphics

        This course gives an introduction to computer graphics, by integrating various skills in computer science such as programming, data structure and algorithm design. With the aid of new human-computer interface, students will learn these fundamental knowledge in computer science in terms of fancy graphics effects that reduce the learning load through abstract data visualization. The content of this course includes raster graphics, interactive graphics, matrix representation of 3D transformation, curve and surface design, ray tracing and visual realism, all with OpenGL source code.

        Lecturer(s)PrerequisitesGrading
        Yongjin LIUlinear algebra, C++ programmingClass attendance is required, Attendance/Presentations (10%), Projects (15% + 20% + 20% + 35%),


x
  • Advance Computer Networks

    This course discusses the basic design principles of the Internet and where the future Internet architecture is heading to. It is for graduate students and we will cultivate the research capabilities of students by this course. 10 lectures will be given. Students are required to read papers and give presentations in the class, as well as finish the course project. The course language is English.

    Lecturer(s)PrerequisitesGrading
    Dan LI-Class attendance is required, Paper reading presentations and Project
xx
xx
xx
  • Human Computer Interaction Technologies

    This course covers the basic understanding of human perception and cognition, interaction styles development, design and evaluation of GUI, and natural human computer interface technologies. Input technologies are emphasized. Multimodality about visual, acoustic and touch sense channels are introduced with new input interfaces. Signal processing, feature extraction, and mapping schemes will also be covered. Measure methods are for the efficiency of interaction. Hands-on laboratories, study reports, individual projects and semester long group projects will be assigned, some can potentially continue as further researches.

    Lecturer(s)PrerequisitesGrading
    Yuanchun SHIProgrammingClass attendance is required, Assignments
xx
  • Process and Methods of Software Project Management

    Software project management is the art and science of planning and leading software projects. It is a sub-discipline of project management in which software projects are planned, monitored and controlled. Using software development life cycle and process groups as two main threads, this course introduces the procedures and methods of software project management, which will help keep projects under control, and produce software with required functions in shorter time, higher quality and predicable cost. This course follows the nine Project Management Knowledge Areas - project integration, scope, time, cost, quality, human resources, communications, risk, and procurement - using the experiences of real-life businesses. The students are organized in groups to do the homework and presentations. Virtual software projects are assigned to groups. Typical scenarios are designed in the projects to help students understand the nine knowledge areas by solving the issues in the projects through group discussion and collaboration.

    Lecturer(s)PrerequisitesGrading
    Yong ZHANGProgramming skillClass attendance is required, Assignments (20%), Mid-term report (15%), Final short show (35%), Final open-book examination (30%)

cxx
  • Topics in Advanced Multimedia Technologies

    The course starts with an overview of the fundamental concepts, algorithms and tools in multimedia processing, including information theory, signal processing, signal compression, and communications. Then, a brief introduction of multimedia related standards such as H.264, HEVC, RTP are provided. Combining the fundamentals and the standards, we will finally introduce the students to challenges and current solutions to various challenges in large scale, real time and mobile multimedia applications.

    Lecturer(s)PrerequisitesGrading
    Jiangtao (Gene) WEN-Attendance/quizzes (25%), Projects/presentations (15%/5% + 15%/5% + 25%/10%)
xx
  • Web Information Retrieval

    This course gives a survey to the new research branches, introduces the state-of-the-art technologies, and discusses on open problems and challenges on Web information retrieval (Web IR). At the same time, the course focuses on the real applications in the Internet environment, making case study and detail analysis on commercial search engines (SE). The main topics of the course includes (but not limited to): IR in Web environment, such as link analysis, anti-spam, etc; question answering; opinion / sentimental analysis; social media and IR; personalized IR and recommendation; user behavior analysis; online advertisement; mobile search; and IR and SE evaluations. The course is composed of lectures and student-conducted discussions.

    Lecturer(s)PrerequisitesGrading
    Min ZHANGProgramming skillClass attendance is required, Assignments, Projects and Tea Time Presentations (bonus score)

xx
  • Introduction to Big Data Systems

    The course starts with an overview of the big data analytics, clustering and distributed programming. We will also cover methods for processing big data as well as optimization techniques. Graph processing and visualization of big data will be covered. There will be labs and projects which allow students to experiment with real data and apply the knowledge of what they learnt in class.

    Lecturer(s)PrerequisitesGrading
    Wenguang CHENSome programming knowledgeAssignments and Labs (50%), Projects (15%), Mid-Term Exam (10%), Final Exam (25%).

xx
  • Advance Network Management

    This course is a graduate course and is primarily project-oriented. It will cover three major of aspects of IP network management: networks, objectives and methodologies. There will be 12 lectures given. Students are expected to form a team of two and finish a project on the THU-INM (Tsinghua University IP Network Management) platform.

    Lecturer(s)PrerequisitesGrading
    Dan PEIC Programming, Undergraduate Network courseDemo of Projects (50%), Technical Reports (30%), Presentation (20%).
  • mod
xx
  • Natural Language Processing

    The course introduces natural language processing (NLP), from its history to recent advances in deep learning applied to NLP. NLP is one of the most important technologies in Artificial Intelligence. NLP aims at enabling computers to understand human languages and communicate with humans. There are a large variety of tasks and machine learning methods in NLP. In this course, we plan to introduce the following subtopics:
    (1) The history and the tasks in NLP. 
    (2) Basic tasks in NLP: Sequence tagging, parsing, classification and clustering.
    (3) Applications in NLP: machine translation, question answering, etc.
    (4) Recent advances in deep learning applied to NLP.
    (5) Open problems and challenges for NLP.
    By learning from lectures and programming assignments, students will master necessary knowledge about NLP and engineering tricks for practical NLP problems.

    Lecturer(s)PrerequisitesGrading
    Zhiyuan Liubasic probability theory; basic linear algebra; programming skills (C++ or Python). Knowledge of machine learning is preferred.Class attendance is required, assignments, projects.
xx
  • Deep Learning

    This course introduces the basic concepts and history of deep learning as well as recent developments. The course will cover the theoretical foundation and typical models of deep learning and discuss the cutting-edge research about deep learning. Specifically, we will introduce the following topics:
    (1) Basics of machine learning
    (2) Multi-layer perceptron
    (3) Convolutional neural networks
    (4) Long short-term memory networks
    (5) Deep generative models
    (6) Generative adversarial networks
    (7) rogramming libraries (e.g., Tensorflow, ZhuSuan)
    The course requires all students to design a deep learning model for solving a practical problem, which can be chosen from a set of problems provided by the teacher or proposed by yourself.

    Lecturer(s)PrerequisitesGrading
    Jun ZHUBasic level of probability theory, linear algebra and calculus. Basic level of programming skills.Class attendance is required, Assignments, Exams/Projects.
xxdvanced Theoretical Computer Science

The course will cover the following topics: NP completeness, PSPACE, L Space, IP system, BPP, derandomization, PCP, classical communication complexity, circuit complexity, Decision tree complexity. quickly recall basics about convex optimization and machine learning: linear/logistic regression, regularization, newton method, stochastic gradient descent (asychronized, variance reduction method), generative vs discrimitive, variance vs bias. Off-the-shelf machine learning and prediction algorithms: k-nn, SVM, kernel trick, clustering, Adaboost, gradient boosting, random forest. Online learning and sequential prediction. Multi-armed bandit, Universal portfolio, Multiplicative weighting method, online convex optimization, basic time series. linear algebra-based learning algorithms: SVD, principle component analysis (PCA), independent component analysis (ICA), Nonnegative matrix factorization (NMF), topic modeling, matrix completion, dictionary learning, tensor method, spectral clustering.

Lecturer(s)PrerequisitesGrading
Andrew YAODiscrete MathmaticsAssignments and Final Exam
xxHot Topics in Computational Biology

The course covers research progress and hot topics in Computational Biology and introduces topics including basic computational theory and methods, three-dimensional structure determination and dynamic study of proteins, protein and  drug molecular design, Proteomics, and Biology evolution model.

Lecturer(s)PrerequisitesGrading
Jianyang ZENGMachine LearningProjects and Final Exam
     

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