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B.Eng. in Artificial Intelligence: Systems and Technologies (AISTN)

Artificial Intelligence (AI) is an emerging engineering discipline that focuses on the technological innovations in enabling computing systems to behave and discover new knowledge with human-like intelligence. It is a broad area that covers many specializations, such as machine learning, deep learning, knowledge representation/inference, large scale computing systems and distributed systems, logic/constraint programming, human-computer interactions, natural language processing, big data analytics, etc. It has evolved in multiple disciplines, such as finance, medicine, manufacturing, robotics, multimedia, telecommunications, computational linguistics, etc., and there is now a huge demand of AI specialists in both local and global employment markets. On the other hand, there are critical challenges on how to innovate and design solid and rigorous solutions for AI, as well as how to properly address the ethical and societal issues with AI.

The AIST programme is designed to meet today’s tremendous need of well-trained talents in AI and related specializations. It aims to equip students with the capabilities of designing and implementing AI systems and technologies that can analyze, reason, and infer knowledge from massive information, backed by rigorous foundations of mathematics, basic sciences, data structures, statistics, algorithms, distributed computing, etc. Such capabilities enable students to develop cutting-edge AI solutions that are of practical interest to academia, industry and society.

Note that the AIST programme aims to admit high caliber students who demonstrate outstanding abilities in English, mathematics and science subjects. Excellent academic backgrounds, together with a problem-solving mindset, will be essential to comprehend the knowledge and tackle the future challenges with AI.

The programme's mission is to equip students with the following capabilities:

  1. reasoning about and inferring knowledge of massive information;
  2. mastering hardware/software primitives for building AI applications, including mathematical modelling, data structures, statistics, algorithms, and distributed computing;
  3. designing and implementing AI applications that can analyze and process data at scale;
  4. applying AI in various disciplines such as finance, medicine, manufacturing, robotics, multimedia, telecommunications, computing linguistics, etc.;
  5. developing research skill sets for making cutting-edge innovations in AI;
  6. considering reliability, safety, privacy, and security issues of AI applications; and
  7. understanding the ethical and societal impacts of AI in human life.

Upon completion of their studies, students will be able to:

  1. identify, formulate and solve AI-related engineering problems (K/S); 
  2. design a system, component, or process to meet desired needs within realistic constraints, such as economic, environmental, social, political, ethical, health and safety, manufacturability and sustainability (K/S/V);
  3. understand the impact of AI solutions in a global and societal context, especially the importance of health, safety and environmental considerations to both workers and the general public (K/V);
  4. communicate and work effectively in multi-disciplinary teams (S/V);
  5. apply knowledge of mathematics, science, and engineering appropriate to the AI degree discipline (K/S); 
  6. design and conduct experiments, as well as to analyze and interpret massive data (K/S); 
  7. use the techniques, skills, and modern computing tools necessary for engineering practice  appropriate to the AI and computing discipline (K/S);
  8. understand professional and ethical responsibility (K/V); and
  9. recognize the need for and the importance of life-long learning (V).

Remarks

K = Knowledge outcomes

S = Skills outcomes

V = Values and attitude outcomes

Every student is assigned an academic advisor who meets with the students at least once a year for purposes of general supervision such as course selection, guided study, adaptation to University learning modes and disciplinary fundamentals, etc.  Students with academic problems or on academic probation / extended probation are required to have a monthly meeting with the academic advisor.