This course offers a survey of the variety of theories and issues in communication in a systematic fashion and from a historical perspective, with a focus on those theories and issues that bear strong implications for the present situations of Hong Kong, Taiwan, and Mainland China. This course seeks to:

• Establish a coherent understanding of the progressive development of the discipline of communication;

• Provide a context for critical appreciation of current scholarship and research in communication;

• Offer a reasonable account of future conditions for human communication.

This course is designed for students without prior programming experience to develop foundational computational thinking skills and practical competence in data science. Core topics include package installation, program execution, variables and expressions, data structures, functions, debugging, and control flow. Students will also gain hands-on experience with key libraries for data exploration, analysis, and visualization.

The increasing presence of artificial intelligence (AI) is changing the land scape of media and communication industry. This course aims to give students a strong practical foundation in deep learning techniques and Large Language Model (LLM) used in digital media. The objective is for students to learn basics of deep neural networks and its application in the field of Natural Language Processing (NLP). This course will introduce basics of neural networks, RNNs, transformers, NLP foundations and related advanced topics, such as LLM, pretraining and cross-modality learning. By the end of course, students are expected to be equipped with practical skills to train their own language models.

This course aims to give students a strong practical foundation in modern computer vision and deep learning techniques used in digital media. It introduces key topics such as image and video classification, CNN architectures, object detection, segmentation, transformers, generative models, and self-supervised learning.

The objective is for students to learn how to design and implement vision-based AI systems and understand their applications in media production and analysis. Through lectures and hands-on labs, students will gain experience building models for image and video processing and evaluating their performance.

By the end of the course, students will be able to understand core principles in computer vision and pattern recognition, build and apply CNN- and transformer-based models for visual tasks, explore advanced topics such as generative and self-supervised methods and assess the capabilities and limitations of AI techniques in digital media.

This course introduces principal concepts of big data analysis and information management. It covers various topics including database management, cloud service fundamentals, data processing, and data analytics. It is expected that students can learn practical skills about how to collect, store, analyze, and process data.

This course aims to introduce students to the fundamental concepts, models, and techniques in artificial intelligence. It provides an overview of the history, applications, and future prospects of AI, followed by core topics including search strategies, regression methods, clustering algorithms, and neural networks.

By the end of the course, students will be able to understand major AI paradigms, explain their underlying logic, and apply core techniques to solve real world AI problems.

In the current age of information overload, recommender systems offer personalized access for users to efficiently search information and make choices online. Recommender systems also play a crucial role in the context of digital media and communication. This course introduces recommender systems’ major concepts, methodologies, evaluation design, and user experiences. A variety of real-world applications in media and communication are included, such as those deployed in news service sites, e-commercial platforms, and social networks.

Data mining is a significant method for media data acquisition, analysis and management. This course aims to introduce the fundamental issues of media d and data mining; to learn the latest techniques of data mining; to conduct application case studies to show the usage of data mining for media analytics. A variety of practical application in media and communication are included, such as social media analysis and social network analysis.

This course aims at providing essential exploratory concepts and techniques for analyzing and visualizing data, and to gain hands-on experience of using software tools for data harvesting, cleaning, storage, analysis and visualization.

This course aims to integrate knowledge and skills of journalism practice in the digital age, by covering computational journalism, news writing and reporting, and data-driven interactive storytelling. Special focus will be placed on the industrial production process of all aspects of techniques, including searching, collecting, analyzing, interpreting, and visualizing data from various sources. The design and aesthetic components will also be covered. In additional to the traditional teaching learning activities, the course will also adopt the case study approach and perform reverse-engineering on classic and cutting-edged data-driven storytelling examples. Critical reflection on the overuse and abuse of big data and relevant ethical and legal controversies will be addressed as well. By the end of the semester, students are expected to produce a data-driven narrative project using computational methods.

Media and communication students need to adapt to the fast-growing world, where technology is playing a increasingly important role. The booming of Internet turns newspaper into a lossy business; The attention blackholes created by social networks grab advertising value from traditional online media; The big data paradigm shifts focus out of traditional sampling survey methods; The Artificial Intelligence powered system unleashes massive production power at scale, and changes working relations and redistribute commercial values in a disruptive way. As a communication specialist in the new era, students need to be technology literate. This course approaches the objective from two folds: For Technology and By Technology. It is highly likely the students will report technology company, event and trend upon graduation. Or they may be PR specialists for a technology company. The duly acquired knowledge about the latest technology from this course can make them more accurate and more confident in the communication process. This is the "for technology" part of this course. Once the students become technology literate, they can create new product, new business model and new workflow, with the help of the new technology. This is the "by technology" part of this course. With the two folds exercises, we expect the students to be serious thinkers and thought leaders in the changing world, not only understanding technology and its influence on communication, but also constantly reflecting the power, its misuse and social implications.

This course will help students apply fundamental newsgathering and writing techniques to real-world reporting. Students will also improve English-language newswriting skills through the analysis of professional news articles and learn to convey information in a concise and engaging style as needed for digital journalism. Though a variety of skills will be addressed, this course focuses on feature writing. In addition, students will create and maintain an online professional portfolio appropriate for a journalist.

This course aims to discover and examine how individuals interact with media on the psychological level. Through exploring (1) media users’ motivations, (2) media processing theories, as well as (3) media effects, this course provides students with an overview of both the theories and methods in the field. In addition, students will go through the complete process of designing and conducting a media psychology study, and apply their findings to solve real-life issues. Topics such as human-computer interactions (HCI) and user experience (UX) will also be covered.

As argued by David Lazer (2015), who is one of the proposers of the manifesto of computational social science, “there is a need to create a new field on social algorithm, which examines the interplay of social and computational code.” This course responds to this call. The course aims to offer students the latest knowledge as well as critical and reflective perspectives on how “algorithm,” i.e., a finite sequence of rules operating on some input yielding some output after a finite number of steps for computer programmes, are shaping, and shaped by, humans’ culture and society. Algorithmic culture is defined as the extent to which people, places, objects, and ideas are ranked, classified, and hierarchized by algorithm-based computational processes. Nowadays algorithm is a crucial component in all aspects of digital communication practice, such as recommender systems, search engines, social media bots, automated content generation systems, immersive media such as virtual reality and augmented reality, and AI-assisted news production systems. This course interrogates how these algorithm-driven media technologies are casting cultural, social, and political impacts on the society, and aims to reveal the power and control hidden behind the algorithmic systems. Special focus will be placed on an array of highly controversial and timely topics, such as the algorithmic discrimination on gender and race, algorithm-confounded cultural values and tastes, censorship, political ideologies, and identities.

The goal of this course is to prepare students with the essential concepts and techniques of algorithms to have knowledge of the benefits of algorithms. Emphasis is placed on developing the students’ capabilities of algorithmic thinking. The course focuses on basic concepts of algorithms, an introduction of popular algorithms, AI algorithms, and algorithmic game theory. The course will also cover the ethical issues and concerns surrounding algorithms. Case studies, particularly the ones related to media, will be discussed throughout the courses. The knowledge will help you to have an informed discussion on the benefits and costs of algorithms.

This course will provide students with a general understanding of the relationship among cognitive neuroscience, physiology and a variety of communication effects. This course is structured into four general sections: 1) implicit measures of attitudes, 2) eye-tracking, 3) physiological measurement systems, and 4) cognitive neuroscience. Cutting-edge research in the areas of the interaction between media audiences and media content, both theories and methods, will be discussed. Students will go through the complete process of designing and conducting a media biology study.

The purpose of this course is to familiarize students with the practical applications and the theoretical implications of social media-related technologies. It presents topics about online relationship in social media. Special emphasis will be placed on connectedness of our social and technological worlds through the lens of graphs and networks. We will look into how entities/agents are connected in social networks; how opinions, disease and political activism spreads through society; how communities form and can be inferred from data, and various other topics. We will study models and theory to help explain and exploit the structure of social networks. We will also discuss some analytical tools to facilitate the analysis of the networks. We may also examine their economic, social, and cultural implications, and cover some of the latest developments in the social media area, and explore techniques for collecting and analyzing online social network data. Through the course students will gain understanding of the social network formed in communication activities and understand the everyday life from a “network perspective”.

Students will learn the concepts, methodologies, and techniques of interactive visualization to facilitate analytical reasoning and critical thinking with data, in order to improve students’ comprehension of data analysis in the field of media and communication field.

This course aims to cover key concepts, technologies and skills in Web and programming design such as Python, R, JavaScript, server side scripting language, database connectivity and session management. After the completion of this course, students will be able to develop Web and social media with dynamic and interactive contents to better understand artificial intelligence’s background and future developments such as data visualization and machine learning.

This course provides students with an overview of how to design and conduct a simple research study (project) for practical application on selected topics in the areas of digital media. It covers study design and the preparation of proposals and manuscripts. Students are introduced to the main types of research methods, with a more in-depth examination of the computational methods, to address research issues in digital media. In this respect, each supervisor could mentor up to 4 students. The work mode could be individual or in team, and students could work on the same topic or different topics. leading to a critical in report and research outputs. The supervisors evaluate their students based on a critical inquiry report and research outputs.