MSc in Data Science with Specialization in Artificial Intelligence Applications
Offered by FST
All courses are compulsory:
CISC7298 Project Report
(Compulsory Course) 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.
Choose 4 required elective courses from the following:
CISC7013 Principles of Artificial Intelligence
3 credits
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
CISC7018 Computer Vision and Pattern Recognition
3 credits
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
CISC7021 Applied Natural Language Processing
3 credits
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
CISC7019 Web Mining
3 credits
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
CISC7022 Big Data Processing and Analysis
3 credits
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
CISC7026 Introduction to Deep Learning
3 credits
This is an introductory course on Deep Learning methods with applications to computer vision, natural language processing, biology, financial data, and more. Students will learn foundational knowledge of deep learning algorithms and get practical experience in building neural networks in modern deep learning frameworks. Experience in Python is helpful but not compulsory. We assume students having background in calculus (i.e., taking derivatives) and linear algebra (i.e., matrix multiplication).