Master of Science in Artificial Intelligence for Executive Professionals [MSc (AIEP)] [*Subject to the University's Approval]

Foundation of Artificial Intelligence

The course explores the basic and advanced concepts of machine learning, including supervised and unsupervised learning, deep learning with a focus on neural networks, convolutional neural network (CNNs), and recurrent neural network (RNNs), generative models like Generative adversarial network (GANs) and autoencoders, and newer architectures such as graph neural networks and transformers. Practical applications in image and speech recognition are highlighted.

Artificial Intelligence Technologies

Focusing on the practical application of AI, this course covers essential AI technologies such as natural language processing (NLP), API integration in AI systems, fine-tuning of pre-trained models, development of chatbots, and exploration of embedded learning models. The course is designed to provide hands-on experience with AI tools and platforms, ensuring students can implement AI solutions effectively.

Humanities and Artificial Intelligence

This course provides a foundational understanding of the key features of both human and artificial intelligence. Students will explore human cognitive processes as interdependent functions that have evolved and adapted to natural physical and social environments. The course delves into the multi-layered aspects of human intelligence, including learning, reasoning, and decision-making, highlighting their unique strengths and weaknesses compared to current AI technologies. Additionally, it examines how AI can support, rather than endanger, human endeavors.

The curriculum offers an in-depth analysis of the complex relationship between human and artificial intelligence, drawing from both Western and Chinese philosophical perspectives spanning 3,000 years. A distinctive feature of this course is its integration of basic neuroscience principles and machine learning. This course will also addresses ethical considerations and promotes the development of responsible AI practices that are mindful of diverse disciplines and contexts.

Contemporary Issues on Artificial Intelligence Technologies

This course is structured to address the most current and advanced topics in the field of artificial intelligence. These areas represent the frontier of AI technology, where significant breakthroughs are shaping the future possibilities of AI applications. The course is designed not only to provide students with knowledge of these cutting-edge technologies but also to develop their ability to critically analyze and evaluate the impact and potential of such innovations. Possible topics for exploration are distributed systems in AI with concepts like cloud computing and edge computing, and the integration of quantum computing with distributed systems to address complex AI challenges. Discussion also include the examination of the ethical considerations and practical challenges associated with implementing advanced computing technologies in AI.

Data Science

This course covers a variety of data structures and various algorithms, including decision trees, linear regression, classification algorithms, and clustering algorithms, to give students an overview of data science.

Spatial Intelligence

This course explores the basic idea of spatial intelligence and spatial awareness in AI. The course introduces fundamental components of spatial intelligence, including AI perception (such as depth sensing), spatial data interpretation, spatial planning and object manipulation. The course will help students understand spatial intelligence across various domains such as robotics, Augmented Reality (AR), Virtual Reality (VR), Geographic Information Systems (GIS), and navigation systems.

Privacy, Security, and Policy

This course centers on the pivotal aspects of privacy, security, and policy in artificial intelligence. Students will explore the intricate balance between innovative AI development and the ethical, legal, and social implications that accompany these advancements. The course includes in-depth discussions on data privacy, highlighting how AI impacts data collection, storage, and usage while considering the privacy of individuals. Security aspects cover the vulnerabilities and threats inherent in AI systems and strategies to mitigate such risks. The course will also discuss about the AI governance, examining current and emerging policies and regulations at both national and international levels.

Trends in Artificial Intelligence at Workplace and at Home

The course provides a broad survey of advanced AI concepts and technologies, with a particular emphasis on their transformative potential across various sectors like healthcare, education, and finance. Students will engage with topics such as the latest advancements in machine learning, the integration of AI in critical industries, the ethical considerations and societal impacts of AI, and speculative futures of AI technology. The pedagogical approach includes dynamic group discussions to debate and analyze new technologies and their implications, alongside a significant research project where students select a specific trend to explore in depth. This project culminates in a detailed analysis of the chosen trend’s current state, its expected evolution, and its potential industry and societal impacts, helping students to not only understand but also anticipate the trajectories of AI technology.

Project I: Developing a Chatbot for Professionals

In this project, students will work in groups to develop a functional chatbot tailored for a specific professional field such as healthcare, law, education, finance, or customer service. The goal is to create a chatbot that understands and responds effectively to industry-specific inquiries, thereby enhancing efficiency and user experience within that field.

Project II: Deploying Personal LLMs / ELMs

Students will individually deploy a personal large language model (LLM) or an efficient language model (ELM) with a focus on customization for personal or small-scale use. The project involves setting up the model, tuning it to specific needs or interests, and evaluating its effectiveness and usability.