Type: Required

Prerequisite(s): N/A

Exclusion(s): N/A

Credits: 1

This course provides guidance to undergraduate students of the AI major for their academic path and future. This course is mostly introductory and aims to inspire UG students for their academic path development and growth of maturity during their UG study. Activities may include seminars, workshops, advising and sharing sessions, interaction with faculty and teaching staff, and discussion with student peers or alumni. Graded P or F.

Type: Required

Prerequisite(s): UFUG 2601 OR UFUG 2602

Exclusion(s): N/A

Credits: 3     

The objective of this course is to present an overview of the principles and practices of AI and to address complex real-world problems. Through introduction of AI tools and techniques, the course helps students develop a basic understanding of problem solving, search, theorem proving, knowledge representation, reasoning and planning methods of AI; and develop practical applications in vision, language, and so on. Topics include foundations (search, knowledge representation, machine learning and natural language understanding) and applications (data mining, decision support systems, adaptive web sites, web log analysis).

Type: Required

Prerequisite(s): DSAA 1001 OR AIAA 2205

Exclusion(s): N/A

Credits: 3

Machine learning is an exciting and fast-growing field that leverages data to build models which can make predictions or decisions. This is an introductory machine learning course that covers fundamental topics in model assessment and selection, supervised learning (e.g., linear regression, logistic regression, neural networks, deep learning, support vector machines, Bayes classifiers, decision trees, ensemble methods); unsupervised learning (e.g., clustering, dimensionality reduction); and reinforcement learning. Students will also gain practical programming skills in machine learning to tackle real-world problems.

Type: Required

Prerequisite(s): UFUG 2601 OR UFUG 2602

Exclusion(s): N/A

Credits: 3

This course introduces core data structure and algorithms, which are fundamental to Data Science and Analytics.

As an important course that bridges students to a number of advanced courses, it covers topics in asymptotic complexity analysis, typical data structures (stacks, queues, trees, and graphs), sorting, searching, data structure-specific algorithms, algorithmic strategies (e.g., divide-and-conquer, greedy, and dynamic programming), analysis and measurement of programs.

Type: Required

Prerequisite(s): N/A

Exclusion(s): N/A

Credits: 3

This introductory course surveys the explosive area of AI ethics and illuminates relevant AI concepts with no prior background needed. Key topics include Fake News Bots; AI Driven Social Media Displacing Traditional Journalism; drone Warfare; Elimination of Traditional Jobs; Privacy-violating Advertising; Monopolistic Network Effects; Biased AI Decision/Recognition Algorithms; Deepfakes; Autonomous Vehicles; Automated Hedge Fund Trading, etc. Through the course, students will be able to understand how human civilization will survive amid the rise of AI, what are the new rules in the new era, how to preserve ethics when facing the threats of extinction and what are engineers’ and entrepreneurs’ ethical responsibilities.

Type: Required

Prerequisite(s): UFUG 1103 OR UFUG 1106

Exclusion(s): N/A

Credits: 3 

This course aims to teach students the basic math concepts for Artificial Intelligence (AI). Key topics include fundamental Linear Algebra (Matrix Calculations, Norms, Eigenvectors and Eigenvalues), Calculus (Derivative, Taylor series, Multivariate Calculus), and Probability Theory (Distributions, Statistics of Random Variables, Bayes’ theorem). With these mathematical concepts, some basic principles of numerical optimization and typical AI algorithms (Gradient Descent, Maximum-likelihood, Regression, Least Square Estimation, Spectral Clustering, Matrix Decomposition, etc.) will also be introduced as examples to better relate math to AI. The approach of this course is specifically AI application oriented, aiming to help students to quickly establish a fundamental mathematical knowledge structure for AI studies. Through this course, students will acquire the fundamental mathematical concepts required for AI, understand the connections between AI and mathematics, and get prepared to learn the mathematical principles, formulas, inductions, and relevant proofs for advanced AI algorithms.

Type: Required

Prerequisite(s):  UCUG1052 OR UCUG 1053

Exclusion(s): N/A

Credits: 3

The course provides students with exposure to and practice in using English within the specific fields of Artificial Intelligence and Data Science. The course aims to encourage students to develop their abilities as thoughtful communication strategists, to communicate effectively, appropriately and confidently in relevant academic and professional contexts, and to use language to express critically the wider social implications of their fields on topics relevant to all AI and DSA students.

Type: Required

Prerequisite(s): UFUG 2601 OR UFUG 2602

Exclusion(s): N/A

Credits: 3       

This course aims to teach the students to program with Python and use Python to develop fundamental Artificial Intelligence (AI) applications. AI-oriented as well as generic programming concepts and skills will be taught in Python language. Key topics include fundamental Python features, principles, and syntax; programming in Python for numerical computation with efficient arrays and matrix classes; programming in Python for scientific analysis with widely adopted scientific libraries; fundamental development of Python web crawler for data collection; data processing and analysis with Python; machine learning model building and evaluation in Python; fundamental usage of deep learning frameworks. Students will practice the programming skills for AI and get familiar with the overall workflow on building AI systems as a team through the course project. Through the class, students will be able to understand programming principles for AI research and development and master the skills to build simple AI applications to solve practical problems.

Type: Required

Prerequisite(s): DSAA 1001 OR UFUG2601 OR UFUG2602

Exclusion(s): N/A

Credits: 3

This course explains the fundamental principles, uses, and some technical details of data mining techniques through lectures and real-world case studies. The emphasis is on understanding the business applications of data mining techniques. The mechanics of how data analytics techniques work will also be discussed as it is essential to the understanding of the fundamental concepts and business applications.

Type: Required

Prerequisite(s): UFUG 2601 OR UFUG 2602

Exclusion(s): N/A

Credits: 3   

Computer vision has attracted a lot of attention in the recent years. Its application scope covers autonomous driving, security surveillance, intelligent industrialization, remote health monitoring, etc. This course will start by providing students with some essential background knowledge, then drive students to dive into the field by participating a mini project that is derived from real world practical needs. Students are encouraged to acquire advanced knowledge through self-learning to accomplish the mini project.

Type: Required

Prerequisite(s): DLED 3020

Exclusion(s): N/A

Credits: 3

The course makes use of the Final Year Capstone Project with its associated spoken and written components to maximize the language and literacy gains from carrying out this significant piece of work. The course supports students in producing the communicative components of the project and, though iterations of those components, further develops and refines their ability to present and write at a high level in an academic context.

Type: Required

Prerequisite(s): N/A 

Corequisite(s): DLED 4020

Exclusion(s): N/A

Credits: 6

This course is an independent, one-year-long final-year project related to Artificial Intelligence. A written report, presentation, and/or examination are required. In the final year, students may wonder what AI research is and whether they are capable of doing research as postgraduate students. This course gives students an opportunity to understand AI research through reference searching, independent critical thinking, academic writing, and presentation. Under the supervision of a faculty member, students will have the opportunity to practice their English communication skills (reading, writing, understanding, and presentation) via project-related activities. Credit load will be spread over the year and can be registered for AI students in their fourth year of study only.