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Academics

Computer Science and Technology ( M.S. )

1. Training/Research Orientation

  • Computer application technology
  • Computer software and theory
  • Computer network and the information security
  • Computer system architecture

2. Program Duration and Credit

Three years generally, the maximum school years are not longer than 5 years (including the extension time).
30 credits of courses in total, at least 19 credits of academic courses.

3. Core Courses and Introduction

Computer Networks Architecture
Course Objectives:
Students are expected to achieve the following objectives through the study of this course:

  • Understanding of the computer networking architecture, especially the horizontal and vertical division and organization of computer networking functionalities
  • The ability to propose solutions for adding a new networking function
  • The ability to analyze the pros and cons of different network designs

Introduction of Course Content:

  • Introduction to Networking Architecture
  • Design Principles
  • Internetworking
  • Advanced Internetworking and Scalability
  • Multicast and Anycast
  • Mobility
  • Network Topology
  • Transport Design
  • Congestion Control
  • P2P and Overlay Networks
  • ID/Locator Split
  • Information-centric Networking
  • Software Defined Networking

Designing and Analysis of Algorithms
Algorithm is an important branch of computer science. The course mainly focuses on algorithm design and analysis, and includes divide-and-conquer algorithms, greedy algorithms, dynamic programming, graph traversal techniques, brute-force methods and so on. The course also includes a series of important algorithmic problems, including sortingproblems, selection problems, minimum spanning tree problem, the shortest path problems, network flow, bipartite graph matching, string matching and geometric algorithms. And It introduces computational complexity of algorithms and NP complete problems.This course is offered for graduate students. Itrequires students to systematically study the basic theoretical conceptsand knowledge of algorithms, and obtain the abilities of algorithm design and implementation. Through studying typical algorithms, it enables students to understand and grasp the basic ideas,methods and techniques of algorithm. And we encourage students to applyalgorithm knowledge to solve all kinds of practical problems, and cultivate students’ computational thinking ability and independent research ability.

Database and Data Mining
The goal of this course is to study the basic theory, the basic principle and the basic methods of database and data warehouse system design, grasp the research contents and the main algorithm of data mining, learn the analysis method of algorithm performance.
The course mainly teaches the basic theory, basic methods and implementation techniques of database and data mining system, and the latest technology and development trend of database, introduce the basic concept and principle of the data warehouse. The research content and the main algorithm of data mining is introduced, including association analysis, classification, clustering analysis, anomaly detection, web mining, methods and algorithms for big data mining. This course teaches programming of classic data mining algorithms and the performance analysis methods of algorithms.

Random Data Analysis
The goal of this course is to provide students with statistical theory and methods for data analysis. Students are expected to be able to perform appropriate statistical analysis on real data with computer software. Main topics of this course include:

  • Data collection
  • Graphical representation of data
  • Descriptive statistics
  • Probability and distributions
  • Samples and Sampling Distribution
  • Parameter estimation
  • Hypothesis testing
  • Univariate linear regression
  • Multivariate linear regression
  • ANOVA and design of experiments
  • Times series analysis and forecasting

Artificial Intelligence
Artificial Intelligence is the important branch of computer science, which aims to create intelligence machines to substitute for some human intelligent behavior. This course will introduce the basic concepts, principles and algorithms in artificial intelligence field. It will cover:

  • State space method and its search techniques for graphs
  • Problem reduction method and its search approaches for AND/OR trees
  • Predicate calculus and resolution principle
  • Prolog language and application
  • Machine learning and application

Upon completion of this course, students should be able:

  • To solve some intelligent problems via designing and developing corresponding software using C/C++ or Prolog programming languages;
  • To solve classification and regression problem using machine learning models;
  • To lay the solid foundations of their further study and research in some related areas;

4. Supervisors

Genlin Ji, MingYang, Futai Zhang, Weiguang Qu, Peiming Bao, Bo Chen, Qing Liu, Yan Sun, Jianhua Xu, Junsheng Zhou, Guoqiang Zhang, Weiling Cai, Qian Gao, Wanfeng Dou, Min Yuan, Yudong Zhang, Yinxia Sun, Bin Zhao, Yan Zhang.

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