| Course Code |
X033518 |
| Course |
Programming Language |
| Credit |
3 |
| Semester |
Spring |
| Target |
Doctor/Master |
| Instructors |
Deng Yuxin |
| Course Description |
The purpose of this course is to introduce some basic principles, methods, and results of programming languages. Particular emphasis will be given to formal semantics and type theory due to the important application of the theory of programming languages as a rigorous foundation for software engineering in formal specification and verification. The main topics covered in the course include among others operational, axiomatic, and denotational semantics of imperative and functional languages with higher-order types, along with some fundamental mathematical techniques used to formalize and reason about programming languages. Beyond exploring the classic semantic theory, the course tries to give some hints on the latest development of programming languages with features such as concurrency and probability. This course provides students an opportunity to appreciate the benefits of the rigorous analysis of programming concepts. After successful completion of this course, students will be encouraged to pursue more advanced topics in formal methods. |
| Dept. Code |
033 |
| Package Module |
CSE |
| |
| Course Code |
C033725 |
| Course |
Image Processing and Machine Vision |
| Credit |
3 |
| Semester |
fall |
| Target |
Doctor |
| Instructors |
Lu Hong Tao |
| Course Description |
The course mainly introduces the basic concept, theory, method and application on Digital Image Processing. Through the study of the course, students can grasp the basic principles of DIP and make a firm foundation for further research in DIP related fields and the following about DIP. The teaching content includes image and image digitizing, algebraic operations, geometric operations, image transform, image restoration, compression, basic principles of pattern recognition and image segmentation. |
| Dept. Code |
033 |
| Package Module |
CSE |
| |
| Course Code |
C033726 |
| Course |
Theory and Methods for Statistical Learning |
| Credit |
3 |
| Semester |
fall |
| Target |
Doctor |
| Instructors |
Zhang Li Qing |
| Course Description |
Statistical learning and inference emphasizes on researching the statistical features of machine learning and inference. The course introduces basic theory and methods to automatically extract rules, patterns and structures in real data and helps students to master the ability to construct model, identify parameters and model inference based on statistical model. Statistical learning has wide applications on data mining, artificial intelligence and natural language processing. Besides the basic theory and methods of statistical learning and inference, the course also provides course design training on large scale data analysis and modeling. The students can obtain preliminary ability to solve large scale real system modeling and learning problems.
The course is suitable to the master students majoring in intelligent information processing, pattern recognition, large scale data mining and bioinformatics. |
| Dept. Code |
033 |
| Package Module |
CSE |
| |
| Course Code |
C033730 |
| Course |
Advanced Ad Hoc Networks |
| Credit |
2 |
| Semester |
fall |
| Target |
Doctor/Master |
| Instructors |
Zhu Yan Min |
| Course Description |
Wireless ad hoc networks present one of the most hot research area in computer networking and mobile computing. A wireless ad hoc network is a self-organizing network consisting of many mobile nodes with wireless communication capabilities. There are many examples of ad hoc networks, e.g., MANET, mesh network, ad hoc networks, delay-tolerant networks, sensor networks, and vehicular ad hoc networks. There is a variety of exciting applications of wireless ad hoc networks, such as battlefield communication, disaster relief, environment monitoring, and wildlife surveillance. In this course, we will introduce basic concepts, architecture, fundamental requirements, key enabling technologies, and typical applications. More importantly, students will survey on the latest research advances. At the end of the course, each student will do a term project, through which the student gets hand-on experiences and get through the whole process of doing a research project of ad hoc networks |
| Dept. Code |
033 |
| Package Module |
CSE |
| |
| Course Code |
F033567 |
| Course |
Network Computing |
| Credit |
3 |
| Semester |
Spring |
| Target |
Master |
| Instructors |
Li jie |
| Course Description |
This course will focus on studying the state of the art in large and distributed networked systems, from both the networking and systems perspectives. |
| Dept. Code |
033 |
| Package Module |
CSE |
| |
| Course Code |
F033572 |
| Course |
Digital Image Processing |
| Credit |
3 |
| Semester |
fall |
| Target |
Master |
| Instructors |
Lu Hong Tao |
| Course Description |
The course mainly introduces the basic concept, theory, method and application on Digital Image Processing. Through the study of the course, students can grasp the basic principles of DIP and make a firm foundation for further research in DIP related fields and the following about DIP. The teaching content includes image and image digitizing, algebraic operations, geometric operations, image transform, image restoration, compression, basic principles of pattern recognition and image segmentation. |
| Dept. Code |
033 |
| Package Module |
CSE |
| |
| Course Code |
F033573 |
| Course |
Scientific Computation Visualization |
| Credit |
3 |
| Semester |
fall |
| Target |
Master |
| Instructors |
Sheng Bin |
| Course Description |
Visualization in Scientific Computing is now a main research direction of Computer Science. It mainly studies the computational methods for converting and exploiting visual information that is easy to be understood for engineering from scientific dada, for examples, measured data, data from computing result, image data from satellite, and medical data from CT and MRI. Visualization in scientific computing has wide spread applications in the fields of molecule modeling, medical image processing, geology science, space exploration, computational fluid dynamics, finite element analysis. Through this course, students are required to learn the research background of scientific data visualization in the world, and the main concepts and algorithms of visualization techniques. Main contents for study include contours extraction in 2D scalar field, surface reconstruction from sections, generation of contour surfaces and rendering algorithms, volume illumination model and volume rendering methods. After this course, students are required to understand the above concepts, algorithms and implementing methods, and have the ability for programming in applications. |
| Dept. Code |
033 |
| Package Module |
CSE |
| |
| Course Code |
F033585 |
| Course |
Wireless Communications and Sensor |
| Credit |
2 |
| Semester |
fall |
| Target |
Master |
| Instructors |
Wu Min You |
| Course Description |
This course provides basics of wireless and sensor networks. Main topics include Introduction to wireless communication, Wireless LANS and PANS, Wireless WANS and MANS, Wireless Internet, and Sensor networks. Upon completion of this course, the students should understand fundamental concepts and techniques of wireless networks and sensor networks |
| Dept. Code |
033 |
| Package Module |
CSE |
| |
| Course Code |
X033503 |
| Course |
Advanced Computer Architecture |
| Credit |
3 |
| Semester |
fall |
| Target |
Master |
| Instructors |
Deng Qian Ni |
| Course Description |
Computer architecture is a vibrant and ever changing area; this course will attempt to convey that to students. It focuses on the design and implementation of computer systems, as well as techniques for analyzing and comparing alternative computer organizations.? We will take the broad view of computer architecture as it evolves - not just CPU design, but the places where hardware and software come together from tiny embedded devices to massive internet service platforms.? Students will learn about styles of computer implementation and organization from a historical and modern perspective. Traditional concepts such as memory hierarchies, pipelining, instruction-level parallelism, data-level parallelism, thread-level parallelism will be discussed. Further, modern issues such as data speculation, dynamic compilation, communication architecture, multiprocessors, and data center will be introduced and discussed. Cutting-edge paradigms such as low-power processors, reliability, and scalable systems will be explored.
In addition to the textbook, this course includes a number of readings from research papers. Such papers are important for a number of reasons, not the least of which is to understand that design decisions are not always black and white.? |
| Dept. Code |
033 |
| Package Module |
CSE |
| |
| Course Code |
X033514 |
| Course |
Computer Graphics |
| Credit |
3 |
| Semester |
Spring |
| Target |
Master |
| Instructors |
Ma lizhuang |
| Course Description |
Computer graphics started with the display of data on hardcopy plotters and cathode ray tube screens soon after the introduction of computers themselves. It has grown to include the creation, storage, and manipulation of models and images of objects. These models come from a diverse and expanding set of fields, and include physical, mathematical, engineering, architectural, and even conceptual structures, natural phenomena, and so on. Computer graphics today is largely interactive: The user controls the contents, structure, and appearance of objects and of their displayed images by using input devices, such as a keyboard, mouse or touch-sensitive panel on the screen. Because of the close relationship between the input devices and the display, the handling of such devices is included in the study of computer graphics. In this course, we will introduce the basic raster graphics algorithms for drawing 3d primitives, geometric transformations in 2D and 3D space, viewing in 3D, representing curves and surfaces, visual reality and computer animation. This course provides the basis for graphics algorithm design, CAD software development and game development. In the experimental class, students will learn and practice the basic algorithms and software systems in the field of Computer Graphics. |
| Dept. Code |
033 |
| Package Module |
CSE |
| |
| Course Code |
X033517 |
| Course |
Computer Networks |
| Credit |
3 |
| Semester |
fall |
| Target |
Master |
| Instructors |
Chen Gui Hai |
| Course Description |
Advanced Networks is not a basic computer-network introduction course, but a paper-oriented, research-oriented, and enjoy-oriented advanced course. The objective of this course is to make students understand the modern networks for computer and computing deeply, comprehend the fundamental methodological issues, and learn how to design a good network. What we emphasize in this course is the analysis of the typical network topologies and methodologies. We will introduce some different kinds of networks, including P2P networks, Data Center networks, Wireless Sensor networks, and Networks in Chips. Besides, before the end of this course, we will give a deep analysis for some very well-known papers from SIGCOMM (the Top One Conference) and introduce some advanced techniques and latest research before the end of this course. |
| Dept. Code |
033 |
| Package Module |
CSE |
| |
| Course Code |
X033524 |
| Course |
Statistical Learning and Inference |
| Credit |
3 |
| Semester |
fall |
| Target |
Master |
| Instructors |
Zhang Li Qing |
| Course Description |
Statistical learning and inference emphasizes on researching the statistical features of machine learning and inference. The course introduces basic theory and methods to automatically extract rules, patterns and structures in real data and helps students to master the ability to construct model, identify parameters and model inference based on statistical model. Statistical learning has wide applications on data mining, artificial intelligence and natural language processing. Besides the basic theory and methods of statistical learning and inference, the course also provides course design training on large scale data analysis and modeling. The students can obtain preliminary ability to solve large scale real system modeling and learning problems.
The course is suitable to the master students majoring in intelligent information processing, pattern recognition, large scale data mining and bioinformatics. |
| Dept. Code |
033 |
| Package Module |
CSE |
| |
| Course Code |
X033526 |
| Course |
Bioinformatics |
| Credit |
3 |
| Semester |
Spring |
| Target |
Master |
| Instructors |
Yuan bo |
| Course Description |
Living things encode their genetic code in DNA, and use this information to regulate biological processes. Bioinformatics is the study of living organisms viewed as dynamical information systems. We study algorithms for sequence alignment, motiffinding and gene finding, and three-dimensional structure prediction. While students can find implementations of many of these algorithms, a study of the algorithms leads to a better understanding of the assumptions and limitations of existing algorithms, and gives students the background to evaluate new ones. We explore some important biological problems, discuss mathematical models, and look at computer algorithms to solve these problems. Most of the interesting problems are intractable, so we look at heuristics. Finally, we take biological information into systems of multiple levels, focusing on the interactions of biological molecules in the contexts of biological networks.? As such, we introduce statistical learning and graphical model and other structural related methods to study biological problems as a whole.? The entire course is biologically motivated while engineering techniques are used as tools |
| Dept. Code |
033 |
| Package Module |
CSE |
| |
| Course Code |
X033531 |
| Course |
Security Engineering |
| Credit |
2 |
| Semester |
fall |
| Target |
Master |
| Instructors |
Lai Xue Jia |
| Course Description |
Basic concepts and advanced topics in cryptography and IT-security. Establish the right understanding of security, attacks and complexity. Principles, structures and methods in the design of the block ciphers DES, IDEA, AES; Explain the strength and weakness in each algorithms and designs. Ideas and methods of varies attacks on block ciphers, main topic is differential attack; Fundamental and construction of iterated hash functions. Attacks on hash functions: pre-image and collision, especially the recent results on MD4, MD5, SHA-1. Concept and methods of authentication. Security requirements on protocols of challenge-response type. The use of standard protocols such as SSL, public-key certificates, PKI, S/MIME in real applications like e-bank, web-security and email. |
| Dept. Code |
033 |
| Package Module |
CSE |
| |
| Course Code |
X033532 |
| Course |
Coding and Information Theory |
| Credit |
3 |
| Semester |
fall |
| Target |
Master |
| Instructors |
Luo Yuan |
| Course Description |
Information and Coding Theory has fundamental contributions to communication theory (data transmission etc.), computer science (data compression etc.), network coding, cryptography, statistical physics and so on. This course has two parts. The first part is of information theory, which includes the measurement of information (entropy, relative entropy, mutual information); weakly typical sequence (for data compression); strongly typical sequence (for data transmission); and Shannon Theorem. The second is of coding theory, which includes linear codes, cyclic codes, Hamming codes, RS codes, decoding principles etc. Furthermore, some basic knowledge about finite field and probability theory will be reviewed. |
| Dept. Code |
033 |
| Package Module |
CSE |
| |
| Course Code |
X033533 |
| Course |
Algorithm analysis and Theory |
| Credit |
3 |
| Semester |
Spring |
| Target |
Master |
| Instructors |
Gao xiaofeng |
| Course Description |
This course is an advanced algorithm class for graduate students. It mainly focuses on the design techniques of various algorithms like divide-and-conquer, greedy approach, dynamic programming, graph algorithm, etc; and the analysis methodology of corresponding designs like amortized analysis, time/space complexity, correctness proof, NP-completeness, and approximations. Upon completion of this course, students will be able to analyze the asymptotic performance of algorithms; demonstrate a familiarity with major algorithms and data structures; apply important algorithmic design paradigms and methods of analysis; and synthesize efficient algorithms in common engineering design situations. |
| Dept. Code |
033 |
| Package Module |
CSE |
| |
| Course Code |
X033536 |
| Course |
Applied Algebraic |
| Credit |
3 |
| Semester |
fall |
| Target |
Master |
| Instructors |
Liu Sheng Li |
| Course Description |
“Applied algebra” introduce application of algebra in cryptography and coding theory. It is a fundmental course in the subject of cryptography and information security. With this course, the students will learn the mathematical background of cryptography. The content is : theory of polynomials and finite field; psudo-random sequence and stream cipher; textbook cryptography and modern cryptography |
| Dept. Code |
033 |
| Package Module |
CSE |
| |
| Course Code |
X033537 |
| Course |
Parallel Computing and Algorithms |
| Credit |
2 |
| Semester |
Spring |
| Target |
Master |
| Instructors |
Guo minyi |
| Course Description |
This course is a practical introduction to parallel programming in C using the MPI (Message Passing Interface) library and the OpenMP application programming interface. It is targeted to upper-division undergraduate students, beginning graduate students, and other students who want to learn this material on their own. It assumes the student has a good background in C programming and has had an introductory class in the analysis of algorithms. The contents of the course include parallel architectures, parallel algorithm design, message-passing programming, performance analysis, matrix-vertor multiplication, document classification, and Monte Carlo methods, etc. |
| Dept. Code |
033 |
| Package Module |
CSE |
| |
| Course Code |
CS26001 |
| Course |
Intelligent Speech Technology |
| Credit |
3 |
| Semester |
fall |
| Target |
Master |
| Instructors |
Yu Kai |
| Course Description |
Speech interface has recently attracted great interest due to the boom of mobile internet. In this course, basic theory, software tools and engineering issues of intelligent speech interaction technology will be taught and discussed. The goal is to allow the students to grasp basic concepts, core mathematical theory and engineering framework and implement a real speech recognition system. Detailed content include: fundamentals of speech interaction system, basic knowledge of speech signal processing, Gaussian mixture model, hidden Markov model, language modeling, decoding algorithm, advanced techniques in acoustic and language modeling, brief introduction of statistical speech synthesis and spoken dialogue systems and tools for speech recognition. There will be course work for implementing a speech recognition system using Linux shell scripts together with open-source tools.? |
| Dept. Code |
033 |
| Package Module |
CSE |
| |
| Course Code |
F033588 |
| Course |
Design and Rendering for Computer Animation??? |
| Credit |
3 |
| Semester |
Spring |
| Target |
Master |
| Instructors |
Sheng Bin |
| Course Description |
Computer Animation is one of the most rapidly expanding areas of creative endeavor and technical development. It contains specialist units in art practice, visual studies and aesthetic theory. This course provides the opportunity to study how to apply art computer technology to the fundamental principles of the art and craft of animation, films, and games. Students learn to create 2D design and 3D modeling with basic animation software. This class may also include the history and theory of computer graphics. The students learn to create animated characters through modeling exercises, storyboard development, artistic conceptualization and intermediate texturing techniques. Students prepare characters for hypothetical game or movie placement. This class also prepares students to complete full professional computer animation projects. Topics covered include texture and background development, modeling, lighting, shading and storyboard preproduction. Students implement and refine the course projects during the semester with the help of the instructor. |
| Dept. Code |
033 |
| Package Module |
CSE |
| |
| Course Code |
X033525 |
| Course |
Machine Learning - Fundamental and Practice |
| Credit |
3 |
| Semester |
Spring |
| Target |
Master |
| Instructors |
Yu Kai |
| Course Description |
Machine learning is to use computer to find rules or perform tasks from data. It is part of the so-called data science and is widely used in both industry and academia. It can, to some extent, remove the use of expert knowledge and explore new knowledge hidden in real world data. This course will focus on basic concepts, fundamental algorithms and practical usage of machine learning algorithms. The primary goal is to deeply understand fundamental concepts and practically master some machine learning algorithms. The topics will cover basic concepts, supervised learning (decision tree, parametric/non-parametric learning, neural network, support vector machine) and unsupervised learning (clustering, dimension reduction) and relevant extensions. As part of a teaching reformation project, the teaching will be heavily combined with two MOOC courses. Students are required to complete the relevant MOOC course and actively participate in classroom discussions. Projects related to the student's own research area will be designed as the final course work. |
| Dept. Code |
033 |
| Package Module |
CSE |