Postgraduate Courses

This course covers advanced programming skills for artificial intelligence (AI) applications. Students will learn to use popular AI libraries such as NumPy, Matplotlib, and Pandas, as well as deep learning frameworks like TensorFlow and PyTorch. Topics covered include data manipulation, visualization, and building neural networks. Prior experience in object-oriented programming is required.

This course offers an in-depth exploration of both the theoretical and practical foundations of artificial intelligence. Students will gain an understanding of fundamental concepts and methods in key AI subfields such as search algorithms, logic, knowledge representation, machine learning, natural language processing, computer vision, robotics, sequential decision-making, and probabilistic reasoning. The course aims to equip students with the necessary knowledge to pursue more advanced AI courses.

This course delves into the ethical considerations involved in designing, developing, and deploying artificial intelligence (AI) systems. Students will investigate the societal impacts of AI and the ethical dilemmas that emerge in areas such as privacy, bias, transparency, and accountability. Through case studies and discussions, students will build a framework for ethical decision-making in the development and implementation of AI systems.

This course presents the fundamental techniques for designing efficient computer algorithms, proving their correctness, and analyzing their running times. General topics include mathematical analysis of algorithms (summations and recurrences), advanced data structures (balanced search trees), algorithm design techniques (divide-and-conquer, dynamic programming, and greedy algorithms), graph algorithms (breadth-first and depth-first search, minimum spanning trees, shortest paths).

This course is an introduction to the foundation of computation, and aims at answering some of the most fundamental questions in computer science: What is an algorithm? What can and cannot be computed at all? What can and cannot be computed efficiently? The topics covered include set theory and countability, formal languages, finite automata and regular languages, pushdown automata and context-free languages, Turing machines, undecidability, P and NP, NP-completeness.

This course offers an extensive overview of traditional and contemporary machine learning paradigms and computational techniques. Learners will delve into the theoretical underpinnings of machine learning and acquire practical skills through engaging in software development projects. The curriculum encompasses both supervised and unsupervised learning methodologies to address a diverse array of machine learning challenges. Additionally, students will be trained in the assessment and selection of suitable machine learning models and will be equipped to implement these strategies to tackle real-world issues.

This course is designed to offer a comprehensive grasp of machine learning strategies tailored for natural language processing, along with their utilization in artificial intelligence contexts. Learners will be equipped to construct and refine an array of machine learning models to address a variety of natural language processing objectives, including sentiment analysis, text categorization, and linguistic translation. A foundational understanding of Python programming and machine learning principles is a prerequisite for this course.

This course offers a thorough comprehension of the machine learning methodologies utilized in computer vision, along with their relevance in artificial intelligence contexts. Participants will be trained in the construction and education of various machine learning models tailored for diverse computer vision objectives, such as image segmentation, object recognition, and the creation of visual content. A prerequisite for this course is familiarity with Python programming and foundational knowledge in machine learning.

This course aims to explore the fast-evolving technologies behind generative Al and large language models. Students will gain a deep understanding of the core concepts and techniques for training these models, as well as practical skills in using them effectively through prompting and integration into broader AI systems. Prior experience in Python programming and machine learning is necessary.

This course offers a comprehensive exploration of the principles, methodologies, and technologies that form the backbone of machine learning systems. It covers essential aspects of building and optimizing these systems, with a balanced emphasis on both theoretical foundations and practical implementations. Throughout the course, we will delve into the design principles of these systems and explore the challenges and opportunities in creating future machine learning systems for next-generation applications and hardware platforms.

This course aims to teach students using artificial intelligence (AI) to solve cybersecurity problems. Students will learn general principles in different cybersecurity domains, e.g., cybersecurity mindsets, cryptographic methods, software security, network security, and hardware security. Moreover, they will also learn how to effectively apply AI techniques to solve problems in cybersecurity domains and contexts.

This course equips aspiring AI entrepreneurs with the essential skills to launch and sustain successful ventures. The course covers entrepreneurial mindset, business planning and development, technology as competitive advantages, financing and investment, and navigating operational, legal and other challenges, complemented by real-world case studies from Hong Kong and beyond. Additionally, students will develop exit strategies to ensure business viability.

This hands-on course explores the real-world challenges of launching an AI-driven startup through detailed case studies of both successful and failed ventures. Students will gain valuable insights into the key factors that determine startup outcomes. Interactive workshops will focus on Shanghai, covering local regulations, essential agencies and organizations. The curriculum combines open-format lectures with practical sessions to provide both theoretical knowledge and actionable skills. Graded P or F.

This course covers selected topics in artificial intelligence and entrepreneurship that reflect recent developments not covered by existing MSc(AIE) courses. The course may be repeated for credit if different topics are covered.

This course aims to equip students with a comprehensive understanding of AI technology innovation management, leadership, and entrepreneurship. It delves into the intricacies of how AI technology innovation processes function, the strategies to effectively lead and manage these processes, and the methods to cultivate an environment that fosters and rewards innovation and entrepreneurial endeavors. Through a combination of seminars, interactive workshops and company visits, students will gain the essential knowledge and skills needed to drive AI innovation and entrepreneurial success in various organizational contexts. Graded PP, P or F.

Students will acquire and practice the project planning and reporting skills essential for completing a group project on a specific artificial intelligence (AI) topic relevant to the MSc(AIE) program. Students are required to complete a final report with oral examination on the final output from the internship or entrepreneurship project and whether the student indeed knows how to apply AI techniques to concrete applications. Graded PP, P or F.