MSc in Data Science with Specialization in Artificial Intelligence Applications
Collaborate with FST
All courses are compulsory:
CISC7298 Project Report
(Compulsory Course) 6 credits
The Project Report is designed to integrate academic theories and cutting-edge technologies through a comprehensive analysis of a case study or academic project. This approach aims to equip students with a structured methodology for problem-solving, as well as to enhance their professional research and development capabilities and project management expertise. The overarching objective is to bridge the gap between theoretical knowledge and practical application, ensuring that academic insights are effectively translated into real-world solutions. Students may engage in collaborative visits to industrial partners, with the alignment of these visits to their project report topics. These interactions provide students with firsthand insights, enhancing the content and relevance of their project reports.
Required elective table (Table A/Table B) for Artificial Intelligence Applications Specialization:
At least 3 Required Electives from the same group.
Table A:
CISC7027 Special Topics in Artificial Intelligence Applications
3 credits
This course introduces special topics and advanced technologies in Artificial Intelligence Applications. The detailed contents may change from year to year depending on current developments and teacher specialization.
ECEN7106 Convex Optimization for Internet of Things Applications
3 credits
This course focuses on convex optimization with applications to wireless communication systems, information theory, signal processing, control systems and machine learning. The first part will be on the theory of convex optimization–recognizing convex sets, convex functions, convex optimization problems and duality. The second part of the course will be on algorithms for solving convex optimization problems. This course is crucial to students and researchers in the above fields of engineering.
ECEN7107 Data Analysis for Internet of Things
3 credits
This course is an introductory course on data analytics and its application in IoT. It covers three major topics: 1) Primary data analytics theory including classification, regression, principal component analysis, etc.; 2) Hands on data analytics experiences with NumPy, Pandas, Matplotlib, & Scikit-learn packages; and 3) Applications in IOT (with a special example on buildings energy systems), in which comprehensive experiments with real data will be included. In this course, students will learn systematic knowledge on data analytics and Python. They will also gain solid hands-on experiences in using Python to analyze IOT data.
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 pattern recognition for postgraduate students. It emphasizes both theory and applications of pattern recognition. Topics include overviews of general pattern recognition techniques, statistical decision theory, linear discriminant functions, multilayer neural networks, supervised learning, unsupervised learning and clustering, and applications of pattern recognition (such as biometrics and multimedia database retrieval.) 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
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
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
The 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.
Table B:
CIVL7045 Intelligent Transportation and Vehicles
3 credits
This course explores the cutting-edge field of Intelligent Transportation and Vehicle Systems, focusing on the integration of advanced technologies with transportation and vehicular systems to enhance performance, safety, and sustainability. Topics include autonomous vehicles, traffic management systems, vehicle-to-vehicle communication, and smart infrastructure.
CIVL7022 Advanced Computational Methods: Principles and Applications
3 credits
This is an introductory course on advanced computational methods for civil and coastal dynamical system analysis. The methods are potentially applied to coastal city vulnerability and resilience analysis, system modeling and updating, dynamical system identification, structural reliability and control, statistical analysis and uncertainty quantification, and more. Students will learn foundational knowledge and principles of advanced computational methods. Also, the students will be equipped with exploring state-of-the-art computational tools and methods to enter the corresponding research fields.
CIVL7206 Innovative Methods and Applications of Information Technology in Construction
3 credits
This course introduces innovative technologies in the construction industry. It covers the advanced developments in construction management strategies, commercial software and digital tools, industrialization techniques, infrastructural systems and facilities, and novel construction methods and materials.
This course also covers the applications of information technology for construction management. Topics include introduction to both well-established information technology solutions and emerging trends. The course employs a combination of lecture and outside reading, and it depends on demonstrations of various software products in each category. Moreover, speakers from various construction companies are invited to discuss the latest implementations in representative construction projects
EMEN7032 Intelligent Theory and Engineering Applications
3 credits
This course introduces the fundamentals of intelligent systems technologies and their broad engineering applications, involving essential topics such as Proportional-Integral-Derivative (PID) control, engineering optimisation, and modeling and control of dynamic systems using neural network techniques. It will introduce the principles of knowledge-based systems, fuzzy logic, artificial neural networks, evolutionary computing and explore how intelligent machines and automation processes could benefit from the application of these technologies. It will also discuss knowledge representation, knowledge acquisition, decision making mechanisms, learning and machine learning, as well as highlight the applications of these technologies in various engineering domains, with particular emphasis on their role in the optimisation of automation processes, control engineering and robotics.
EMEN7039 Prognostics and Health Management of Engineering Systems
3 credits
This course introduces the concepts, methods and applications of prognostics and health management (PHM) for engineering system. A variety of tools and techniques for developing health management and monitoring of components and systems will be discussed. Topics include sensor signal acquisition, signals processing, feature extraction, fault diagnosis, data driven prognostics models, reliability, intelligent maintenance, and applications of artificial intelligence in prognostics and health management.
OCES7001 Ocean Remote Sensing
3 credits
This course is designed to introduce students to important concepts and fundamental principles in remote sensing and applications in oceans and coastal environments. Topics cover sensors, microwaves, image analysis, applications of remote sensing in oceans and coastal area, and application of artificial intelligence in ocean remote sensing.
OCES7005 Marine Robotics and Application
3 credits
This course introduces the broad spectrum of marine vehicles and their applications in ocean environments, the fundamental principles of autonomous underwater and surface vehicles, as well as theoretical and practical design considerations for marine robotic systems.