MSc in Data Science with Specialization in Artificial Intelligence Applications
Collaborate with FST
All courses are compulsory:
CISC7298 Project Report
6 credits
Project Report focuses on combining existing academic theories or advanced technologies with an evaluation of a case study or academic project. Students will learn systematic problem-solving process skills, professional research and development practices, and project management skills. The goal of this option 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 Traditional Artificial Intelligence Principles: Problem Solving, and Logical Agent. Overview of Modern Artificial Intelligence Principles: Machine Learning, Decision Tree, Neural Networks, Support Vector Machines, and Introduction to Deep Learning. 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 computer vision and pattern recognition. Topics include Bayesian decision theory, support vector machine, image features, image stitching, component analysis, neural networks and deep learning. 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, Nävie 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 course introduces Deep Learning (DL) basics, methods, and algorithms, with hands-on practice using modern DL library tools (e.g., PyTorch). After the introductory lecture on deep learning, the course first covers the fundamental of neural networks, including universal approximator theory, learning neural networks, backpropagation, optimization, stochastic gradient descent, and tricks on training neural networks, and then focuses on typical neural network architectures, including Convolutional Neural Networks, Recurrent Neural Networks, and Generative Neural Networks.