Chương trình đào tạo bằng tiếng Anh
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Danh sách các môn học Cao học đang được giảng dạy bằng tiếng Anh, ngành Khoa học máy tính

Trí tuệ nhân tạo nâng cao
Advanced Artificial Intelligence
Aims: Artificial Intelligence (AI) is a discipline aiming at realizing intelligence
behavior on computers. This lecture deals with formal treatments of human knowledge
and automatic learning mechanisms for acquiring novel knowledge from various types of
data and environments.
Contents: Logic programming, non-motonic reasoning, machine learning
1. Introduction
2. Logic programming
3. Resolution and Refutation
4. Revision of Uncertain Knowledge
5. Logic of Rational Agents
6. Fundametal Logic
7. Concept Learning
8. Decision Trees
9. Learning of Rules
10. Supervised Learning and Unsupervised Learning
11. Neural network
12. Genetic Algorithm
13. Reinforcement Learning
14. Examination
Phương pháp luận thiết kế phần mềm
Software Design Methodology(e)
Aims: To enable students to design and implement various types of information
systems with easy-to-change and reusable. We study object-oriented analysis, objectoriented
design, and obect-oriented programming technologies.
Contents: Basic concepts in object-oriented technologies, unified modeling
language (UML), usecase modeling, designing static structure of a system, designing
dynamic behavior of a system, architectural and design patterns, object-oriented
programming techniques.
1. Introduction
2. Basic Concepts
3. An overview of UML
4. Usecase modeling
5. Static models
6. Dynamic models
7. Real time systems and object-oriented technologies
8. Physical architecture design
9. Design patterns and UML
10. UML process
11. Case study
12. Examination
Xử lý ngôn ngữ tự nhiên
Natural Language Processing
Aims: A corpus is a collection of a large amount of sentences excerpted from
newspaper articles, magazines, novels, technical papers, and so on. The aim of this
lecture is to study natural language processing techniques using corpora, called
corpus-based natural language processing.
Contents: The major topics of the lecture are as follows.
- Disambiguation using corpora: Disambiguation is one of the major problems in
natural language processing, which is to choose the correct result of natural
language analysis among a lot of candidates. The lecture will introduce
disambiguation techniques to rank candidates using statistical information
obtained from corpora in various topic of natural language processing, especially
part-of-speech tagging, syntactic analysis, identifying word sense etc.
- Knowledge acquisition for natural language processing: The lecture will
introduce methods to acquire knowledge resources for natural language
processing such as a grammar, thesaurus and case frame dictionary and so on.
- Example-based natural language processing: the lecture will introduce an
example-based natural language processing, an approach which regards a
corpus consisting of analyzed sentences as an example database and analyze
new sentence using it.
1. Introduction
2. Foundation of statistics
3. Probabilistic language model
4. part-of-speech tagging
5. Prepositional phrase attachment
6. Statistical parsing (1)
7. Statistical parsing (2)
8. Word sense disambiguation
9. Knowledge acquisition (case frame)
10. Knowledge acquisition (grammar)
11. Knowledge acquisition (thesaurus)
12. Text categorization
13. Bilingual corpus, alignment
14. Example-based natural language processing
Cơ sở dữ liệu nâng cao
Advanced Topics in Database Systems
Aims: The course will introduce the basic knowledge on a number of advanced
topics in database systems with the concentration on data mining. It will deliver to
students the concepts and techniques in distributed database systems, data mining and
data warehousing.
Contents: Distributed database system architecture, Distributed database system
design, Distributed query processing and optimization, Distributed transaction
management, Association analysis, Classification and prediction, Cluster analysis,
Mining complex types of data, Data warehousing and OLAP technology for data mining.
1. Distributed database system architecture
2. Distributed database system design
3. Distributed query procesing and optimization
4. Distributed transaction management
5. Mining association rules (I)
6. Mining association rules (II)
7. Classification and prediction (I)
8. Classification and prediction (II)
9. Cluster analysis (I)
10. Cluster Analysis (II)
11. Mining complex types of data (I)
12. Mining complex types of data (II)
13. Data warehousing and OLAP technology for data mining (I)
14. Data warehousing and OLAP technology for data mining (II)
15. Examination
Thông tin hình ảnh và tương tác người–máy
Image Information Science and Human Communication
Aims: We will understand what is an image information considering a definition
of information content, strage transmission efficiently and evaluating correctly.
Especially, we will describe not only about statistics of image but also about high order
sensation of human system and an advanced processing system. In addition, we will
overview a color engineering and image systhesis of CG.
Contents: Fundamentals of Image Information, Image Coding, Color Engineering,
Image Synthesis.
1. Bases of Image Information (Image Communication model, feature of
Image, Image Coding).
2. Bases of Image Information (Image Type, Sampling, Feature and
Information content).
3. Bases of Image Information (Visual Perception, Quantity of Percepted
Information and image Data).
4. Television Standards (NTSC, Signal Spectrum, EDTV).
5. Television Standards (HDTV, MUSE).
6. Fundamentals of Image Coding (overview 1) (Statistical property of image
data, Redundancy, Basic method of Image Coding (DPCM, OTC,
Hadamard Transform, COS transform, K-L transform)).
7. Fundamentals of Image Coding (overview 2) (Basic method of Image
Coding (Legendre transform, Wavelet transform, JPEG, MPEG, VQ),
Model based Coding).
8. Midle term examination.
9. Fundmental of Image Coding (Random Field for Image data, Optimum
Coeficients of Estimation for DPCM, Rate Distortion theory).
10. Fundamentals of Image Coding (Picture quality evaluation).
11. Color Engineering (Bases, Color perception).
12. Color Engineering (Uniform Color Space, Munsell Color Space, Color
Difference, Applications).
13. Image Synthesis (CG, Rendering, Shading, Mapping).
14. Image Synthesis (Modeling, Reality, Photo-real CG, non-Photo-real CG).
15. Examination
Tác tử thông minh
Intelligent agents
Aims: We study in this course how various AI tecniques can be integrated into the
design of an intelligent agent that can obtain information from the enviroment, carry out a
task, and communicate with humans.
Contents: Intelligent agent, problem solving, knowledge and inference, planning,
learning, language understanding, dialog processing.
1. Intelligent Agents.
2. Probelmsolving Agents.
3. Agents that Reason Logically 1.
4. Agents that Reason Logically 2.
5. Building a Knowledge Base
6. Building a Knowledge Base
7. Planning Agent 1.
8. Planning Agent 2.
9. Uncertainty and Reasoning
10. Uncertainty and Reasoning
11. Learning 1.
12. Learning 2.
13. Agents that Communicate 1
14. Agents that Communicate 2
15. Examination.
Kiến trúc phần mềm
Software Architecture
Aims: Software Architecture is a state-of-the-art topic for improving producttivity
and reliability of information Systems. We don’t build systems from the scratch, but
build a structural platform (called Architecture) and place many parts on it which
achieves some functiions (called components) especially for complex and distributed
applications on the Internet. You study a modern software development mehodology
based on software architecture and components, through some practical and concrete
Contents: basic concepts of architecture/components, partterns,frameworks,
component mechanisms, implementations.
1. Introduction (Methodology, Reuse, from library to Object-Oriented)
2. Basic concepts (Architecture, patterns, Components).
3. Java Revised (1) Inhertance and Delegation.
4. Java Revised (2) AWT Event model, Name spaces.
5. Client-side Architecture (1) Java-Beans, Applets.
6. Client-side Architecture (2) MVC.
7. Exercise 1 (Client-side).
8. Server-side Architecture (1) JavaRMT/CORBA IDL.
9. Server-side Architecture (2) JSP/Servelet/Tomcat.
10. Exercise 2 (Server-side).
11. Web Technologies (1) Overview, HTML/XML/ 3/4tier models.
12. Web Technologies (2) Deployment and security, DB access.
13. Exercise 3 (Web application).
14. Current topics Web Services, .NET, SOAP, WSDL.
15. End term examination.

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