Overview of Artificial Intelligence Application Areas, Languages and Programming Techniques for Artificial Intelligence, Problem Solving, Knowledge-based Systems, Knowledge Representation, Planning, Machine Learning, Natural Language Processing, Genetic Algorithms. Read More
This course introduces the fundamentals and advanced topics of computer vision and pattern recognition for postgraduate students. It emphasizes both theory and applications of pattern recognition. Topics include overviews of general computer vision and pattern recognition techniques, statistical decision theory, linear discriminant functions, multiplayer neural and deep networks, supervised learning, unsupervised learning and clustering, and applications of computer vision and pattern recognition. Read More
This course covers both the fundamental and advanced topics in Natural Language Processing (NLP), which deals with the application of computational models to text data. In this course, the core tasks in natural language processing will be examined, including minimum edit distance, language modelling, Naive Bayes, Maximum Entropy, text classification, sequence labelling, POS tagging, syntax parsing and computational lexical semantics. Modern NLP applications will be explored such as information retrieval, and statistical machine translation. Students will learn how to formulate and investigate research questions on related topics. Read More
The course will cover the fundamental concepts, principles and algorithms in the area of Web Mining. It will firstly give an introduction to the concepts of the traditional information retrieval systems and the principles of web search engines, then, the course will extensively discuss techniques and algorithms of web mining, including Link-Base analysis, web page classifications, web advertisement, recommendation algorithms, web information extractions, web image indexing. The course also requires each student to complete a related course project. Read More
This course introduces the latest development of data engineering techniques, including data query processing (e.g., multi-dimensional data, sequence data, and spatial-temporal data) in cloud computing and HPC environments. Students will learn study and learn how to formulate and investigate the state-of-the-art problems and solutions on related topics. Read More
Project Report 6 credits
An independent project under the supervision of a faculty staff member. A Project Report focuses on combining existing academic theories or advanced technologies with an evaluation of a case study or industrial project. The goal of this Project Report is to facilitate the integration of practice with academic research.