Course Descriptions

Artificial intelligence (AI) is a new technical science that studies and develops theories, methods, techniques, and application systems for simulating and extending human intelligence. AI techniques and models have been widely employed in various domain-specific applications due to their promising performance compared to conventional methods. This course focuses on fundamental concepts, techniques, and potential business applications of artificial intelligence. The course provides an overview of waves of AI, intelligent agents, problem-solving, planning, reasoning, learning. It includes topics about search, logic, genetic algorithms, computational learning methods, and some potential business applications like expert systems, news analysis, and so on.

This course is designed to describe the advanced concepts and principles of data management for business. Various types of databases will be discussed in this course, such as object-oriented, relational, document-oriented, NoSQL, and New SQL. Popular database management systems such as Microsoft SQL Server and/or Oracle will be described. Topics include data models (ER, relational, and others); query language (Structure Queries Language); management of semi-structured and complex data; NoSQL databases. It also covers the essential concepts, options, and best practices for data administration, data protection, privacy control, user security and management, and system configurations. It addresses topics about the general concepts of data disaster recovery, planning, and procedures.

This course provides students with the knowledge of the business data analytics process as well as the fundamental principles of programming for data collection, data preprocessing, data analysis, and data visualisation. It introduces different concepts of Python programming, including the basic Python language syntax, variable declaration, basic operators, program flow and control, defining and using functions, classes, and file and operating system interface. Basic Python packages designed for data analytics will be introduced, such as Numpy, Scipy, Matplotlib, and Pandas. A number of data analytics applications in different business domains will be described.

Machine learning is a branch and one of the most popular AI techniques in recent years. Machine learning models and techniques have been widely used in many fields, such as natural language understanding, machine vision, and pattern recognition. This course will introduce the concepts, techniques, and business applications of machine learning. The course will cover the supervised, semi-supervised, unsupervised, transfer, and reinforcement learning paradigms. The techniques include regression, probability generative model, logistic regression, neural networks, support vector machine, Q-learning, and so on. The business application examples of these courses will be included and introduced in this course.

Deep learning is one of the bleeding-edge technologies of machine learning. It is a neural network used to establish and simulate the human brain for analytical learning and to interpret data by imitating the mechanism of the human brain. Deep learning is widely used in computer vision, speech recognition, natural language processing, and other fields. This course aims at providing an intensive understanding and hands-on experience of the existing deep learning approaches. The topics will cover how to select deep neural networks, how to design deep neural networks, and how to train and optimize the neural networks for practical applications. The course will cover deep neural network models, including convolutional neural networks, recurrent neural networks, long short-term memory networks, deep residual networks, generative adversarial networks, attention-based models, adversarial learning models, and training techniques including dropout, batch normalisation, selection of activation functions and so on. TensorFlow, Pytorch, or other state-of-the-art deep learning tools will be introduced and applied to solve different classes of problems with huge datasets in business domains.

This course provides an understanding of the concept and challenge of big data. The focus is on the data analytic techniques to tackle the V’s (volume, velocity, variety, veracity, valence, and value) in big data and how these impacts data collection, monitoring, storage, analysis and reporting. The following topics across the big data domain will be introduced: distributed file systems; big data analysis techniques; high-performance processing algorithms for big data; big data search and query technologies. An example (Apache Spark) of big data management system to manage and process large-scale data is introduced in the course. Real world big data analytics applications in domains like business will also be elaborated. Students will actively participate in the delivery of this course through assignments, portfolio development, and projects.

The integrated application of AI techniques and business analytics to solve real-world problems is an essential capability in contemporary professional practice. This course provides students with the opportunity to synthesise and apply knowledge gained in earlier courses through the preparation, analysis, interpretation, reflection, and communication of data in a selected research or application context. Emphasis is placed on the planning, management, and execution of a well-defined group project of appropriate scope and complexity.

Projects are undertaken in groups under the guidance of supervisors, who provide the project topics and define the problem context, objectives, and expected scope. Depending on the project, students may work with real-world or experimental data and apply appropriate methods to investigate, design, implement, and evaluate AI- and analytics-driven solutions.

Typical project areas may include large language model (LLM) applications in education, business, healthcare, and social wellbeing; retrieval-augmented generation (RAG) systems for domain-specific knowledge support; agentic AI solutions for decision support and task automation; multimodal AI for text, image, audio, and video analysis; sentiment analysis and topic modelling for customer insight and public opinion analysis; AI-enabled recommendation and personalisation systems; computer vision applications for monitoring, detection, and quality assessment; predictive analytics for finance, operations, and risk management; AI solutions for smart cities and urban analytics; digital health analytics using physiological and behavioural data; and human-centred AI applications that enhance user experience, accessibility, and social impact.

This course introduces the foundation of mobile technology and the basics for developing mobile applications. The course is also designed for managers to appreciate the business value of innovations in mobile technology as well as the relevant ethical issues.

This course is about the fundamentals of social media for e-business and the steps involved in incorporating social technology into an e-business platform. It equips students with a comprehensive understanding of social media applications and their contribution to the formulation and corporate strategies.

The principles of project management, largely developed and tested on engineering projects, are being successfully applied to projects of all sizes and types within the business world. Furthermore, the role of project management in a cross section of applications such as information technology, product development, and construction is now emphasized. This course addresses the fundamental principles of project management, and the tools and techniques at our disposal to help achieve our goals. Topics covered include: project definition and start up; project attribute estimation; planning and scheduling; resource selection and allocation, implementation; post-project evaluation; project management as a career; skills and knowledge required by professionals, including decision-making and resource allocation appropriate to project phases; integration with other disciplines, including accounting and finance. The Microsoft Project software tool will be introduced for project scheduling and management.

Organizations often need to make decisions in their best interests in different situations, and Microsoft Excel is one of the most popular software that business people use to assist their decision making. This course introduces commonly used quantitative analysis techniques that facilitate scientific and systematic decision making. Students will learn how to employ appropriate decision making techniques to obtain the best solutions for a variety of business problems, and learn about the best-practices of spreadsheet modelling for clarity and communication. Through practicing these techniques and Excel functions, students will develop analytical and computer-based problem-solving skills, which can help them improve their performance at work or in daily life.

In an optimization problem, one seeks to minimise or maximise an objective function or a number of objective functions with real, integer, and/or discrete variables, subject to constraints on the variables. Optimization refers to the study of these problems, their properties, the development and implementation of algorithms to solve these problems, and the application of these algorithms to real-world problems. In this course, advanced artificial intelligence algorithms such as multi/many objectives optimization algorithms, genetic algorithms, evolution strategies, ant colony optimization, particle swarm algorithms, differential evolution, and other meta-heuristic methods will be discussed. These methods are able to find optimal or near-optimal solutions for challenging optimization problems. This course will also describe some real-world applications that use these algorithms to handle difficult real-world problems.

Blockchain, as a decentralised open ledger, has proven to be a phenomenal success. This ground-breaking technique holds a huge promise in various fields, digital identification, data marketing, cryptocurrencies like bitcoin, etc. This course introduces students the fundamentals of blockchain, distributed ledger technology, alternative consensus, smart contracts and security, and cryptocurrencies. Case studies of cryptocurrencies and examples of application (e.g., Bitcoin) will be also elaborated. Students will understand the impact of blockchain technologies on financial services and other industries through assignments and projects.

Healthcare analytics transform the traditional medical system in an all-round way, making healthcare more efficient, more convenient, and more personalised. This course will introduce student the key technologies that support smart healthcare. It explains how to build the surveillance infrastructure and how the data is collected and transmitted back from various wearable sensors of multiple sources, by using the technologies of Internet of Things (IoT): MAC protocols, routing protocols. This course will also describe data fusion of health and healthcare data, data models, data management, machine learning algorithms, and analytics techniques and tools for health risk prediction. Case studies and examples of application will be elaborated in this course.

Marketing analytics is the intersection of Marketing and Data Science, generating business insights and offering new opportunities for a competitive advantage. New digital technologies have fundamentally changed various aspects of marketing practice over the past years and have led to a dramatic shift in the quantity and quality of information we are able to access, analyse, and act. The course discusses the cutting-edge techniques used to unlock the predictive potential of data analysis to enhance marketing performance, strategic management, and operational efficiency and provides students with hand-on experience in the application of analytical tools and techniques, to real-life marketing problem.

With the rapid development of cloud resources, majority of traditional data centers has been replaced by cloud platforms (such as Amazon Web Services, Microsoft Azure, Google Cloud Platform) due to the limited bandwidth of resource. A qualified data scientist needs to equip with cloud computing skills, learning to perform a series of tasks in the data pipeline on the cloud such as data acquisition, data cleansing, data transformation and data mining, as well as model training and testing. This course aims to examine the latest trends of cloud computing and provide students with the fundamental knowledge of cloud computing. Students will learn the best practices in deploying and implementing cloud computing applicable for unique business requirements. The course discusses the conceptual topics of cloud technologies and provide hands-on experience through projects utilizing public cloud infrastructures. Topics include cloud delivery models (SaaS, PaaS, and IaaS); Cloud computing overview; Public cloud infrastructure, On-demand self-service, and resource pooling; rapid elasticity; measured service; cloud storage architecture (data distribution, durability, consistency, and redundancy); data deduplication; cloud security issues; case studies of current cloud computing platforms.

This course is an introduction to Natural Language Processing (NLP). It covers a brief overview of the field, including the cutting-edge text processing tasks (e.g., text summarization, named entity recognition, document classification, etc.), their computational problem setting and general thoughts of methodologies. State-of-the-art techniques will also be discussed, including generative sequence-to-sequence models, multimodal data modelling (e.g., image-to-text, video/audio-to-text), chatbot, question-answering system, topic modelling, etc.

This course will introduce the techniques for visual data processing and analysis. Topics include image processing and analysis in spatial and frequency domains, image restoration and compression, image segmentation and registration, morphological image processing, representation and description, feature description, face recognition, iris recognition, fingerprint recognition, image analysis topics, such as medical image analysis.

This course introduces the fundamental visualization techniques to transform complex data sets into understandable and insightful visual representations for the purpose of data storytelling.

The curriculum spans across a range of topics, including the design principles, human visual perception, open-source visualization tools, visualization techniques for different types of data. The course adopts a hands-on approach, incorporating practical exercises using popular data visualization tools like Tableau and Python. It also emphasizes the importance of data preparation and cleaning, ensuring students understand the entire data visualization process from data collection to final visualization.

Throughout the course, students will be tasked with creating their own data visualizations, culminating in a group project where they will present a data story using the skills learned.

This course provides an in-depth exploration of large language models (LLMs), focusing on their principles, architectures, training methodologies, and applications. Students will learn about the evolution of LLMs, from early models to state-of-the-art systems like GPT-4. The course covers key concepts such as natural language processing, transfer learning, and ethical considerations in AI. Through hands-on projects and case studies, students will gain practical experience in developing and deploying LLMs across various domains.

This course explores the integration of generative AI tools in learning and applying programming skills. Students will discover how to leverage state-of-the-art generative models to enhance their coding proficiency and develop innovative applications. The curriculum covers foundational programming concepts, practical implementation strategies, and ethical considerations. Through hands-on projects, students will gain experience in using generative AI to assist with coding, debugging, and project development, preparing them for advanced programming challenges.