Tsinghua Computer science as at Jan 2019 -faculty

 Machine Learning

• 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)	Prerequisites	Grading
  Kang CHEN	data structure, C programming	Class 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)	Prerequisites	Grading
    Ling FENG	Database Management Systems	Class 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)	Prerequisites	Grading
      Yongjin LIU	linear algebra, C++ programming	Class attendance is required, Attendance/Presentations (10%), Projects (15% + 20% + 20% + 35%),

    • 
      
      

• 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)	Prerequisites	Grading
  Dan LI	-	Class attendance is required, Paper reading presentations and Project

• 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)	Prerequisites	Grading
  Yuanchun SHI	Programming	Class attendance is required, Assignments

• 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)	Prerequisites	Grading
  Yong ZHANG	Programming skill	Class attendance is required, Assignments (20%), Mid-term report (15%), Final short show (35%), Final open-book examination (30%)

• 
  

• 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)	Prerequisites	Grading
  Jiangtao (Gene) WEN	-	Attendance/quizzes (25%), Projects/presentations (15%/5% + 15%/5% + 25%/10%)

• 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)	Prerequisites	Grading
  Min ZHANG	Programming skill	Class attendance is required, Assignments, Projects and Tea Time Presentations (bonus score)

• 
  

• 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)	Prerequisites	Grading
  Wenguang CHEN	Some programming knowledge	Assignments and Labs (50%), Projects (15%), Mid-Term Exam (10%), Final Exam (25%).

• 
  

• 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)	Prerequisites	Grading
  Dan PEI	C Programming, Undergraduate Network course	Demo of Projects (50%), Technical Reports (30%), Presentation (20%).

• mod

• 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)	Prerequisites	Grading
  Zhiyuan Liu	basic probability theory; basic linear algebra; programming skills (C++ or Python). Knowledge of machine learning is preferred.	Class attendance is required, assignments, projects.

• 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)	Prerequisites	Grading
  Jun ZHU	Basic level of probability theory, linear algebra and calculus. Basic level of programming skills.	Class attendance is required, Assignments, Exams/Projects.