Pre-requisite(s):
None 

Course Description:
The course aims to present a multidisciplinary forum and expose students to contemporary understanding on how mental processes such as visual perceptions, memories, attentions, languages and thoughts are implemented in our living brain, paving the way for their future creative work and research in the field of artificial intelligence.

Pre-requisite(s):
None 

Course Description:
This course aims to illustrate lawful relations between perceptual experiences and the physical world and to develop models of the processes and mechanisms that produce these connections. We will discuss fundamental problems in perception and learn how the latest technology allows us to measure the brain's responses to various sensory stimuli, and how conscious effort and experience can affect these responses.

Pre-requisite(s):
COMP1013 STRUCTURED PROGRAMMING, or
GCIT1013 FOUNDATIONS OF C PROGRAMMING, or
COMP2003 DATA STRUCTURES AND ALGORITHMS, or
AI2003 DATA STRUCTURES AND ALGORITHM ANALYSIS

Course Description:
This course aims to introduce students to the concepts involved with autonomous robotic systems. The objective of this course is to use a hands-on approach to introduce the basic concepts in robotics, focusing on mobile robots.

Pre-requisite(s):
AI2033 PROBABILITY AND STATISTICS, and
AI2003 DATA STRUCTURES AND ALGORITHM ANALYSIS or COMP2003 DATA STRUCTURES AND ALGORITHMS

Course Description:
Bayesian networks, also called belief networks or Bayes nets, are probabilistic graphical models for representing knowledge about an uncertain domain. This course aims to facilitate the students to develop the knowledge and skills necessary to effectively design, implement and apply Bayesian networks to solve real problems. The course will cover (a) Bayesian networks representations; (b) exact and approximate inference methods; (c) estimation of both the parameters and structure of graphical models. Students entering the class should have good Programming skills and knowledge of algorithms. Undergraduate-level knowledge of probability and statistics is required.

Pre-requisite(s): None 

Course Description:
The focus of this course is to provide the basic concept and knowledge of Intelligent Agent Technology (IAT) and how such cutting-edge technology can be applied and adopted in our daily life and works. It teaches students the basic concept and theories of Intelligent Agent Technology (IAT); the core AI enabling technologies for the support of IAT; the major intelligent agents and mobile agent applications; and the design and development of intelligent agent and mobile agent applications with the adoption of Java Agent Development Environment (JADE).

Pre-requisite(s): None 

Course Description:
The primary objective of this course is to exposes the students to the neural processes, biological substrates and cognitive functions of the human brain, as well as up-to-date neural methods in the neuroscience including brain imaging, electrical measurement and stimulation of the brain, and eye-tracking techniques. The course will also introduce computational algorithms to examine and/or simulate simple neurocognition. Students will develop an advanced understanding of the biological bases of neural network, paving the way for their future creative work and research in the field of artificial intelligence.

Pre-requisite(s): None 

Course Description: 
The course is designed to introduce the most important and basic concepts, methods, and tools used in Bioinformatics which includes an introduction to Bioinformatics, experience with select bioinformatics tools and databases currently utilized in the life sciences.

Pre-requisite(s): None 

Course Description:
The artificial intelligence project allows students to create a usable/public software system with technology in artificial intelligence that can be used to show their professional skills to potential employers. Students engage in an independent problem-solving activity under the supervision of a faculty member or with an industrial partner. The project demands careful planning and creative application of underlying theories and enabling technologies in artificial intelligence. A report, a software/hardware system and an oral presentation are required for successful completion of the project. The projects are drawn from real-world problems and are conducted with industry, government, and/or academic partners.

* Students can take only one of these ME courses, i.e., the “AI3083 Artificial Intelligence Project” and “AI4043 Artificial Intelligence Internship” courses, which could also be double counted as GE Capstone courses. These courses allow students to gain more practical experience by participating in the research projects of UIC staff or that of our industrial partners. Students will need to identify a teacher who is willing to serve as their project supervisor before registering for the course.

Pre-requisite(s): None 

Course Description:
The focus of this course will be on basic semantic technologies including the principles of knowledge representation and symbolic AI. This includes information encoding via RDF triples, knowledge representation via ontologies with OWL, efficiently querying knowledge graphs via SPARQL, latent representation of knowledge in vector space, as well as knowledge graph applications in innovative AI systems such as semantic and exploratory search engines.

Pre-requisite(s):
AI3013 MACHINE LEARNING

Course Description:
The course will provide an introduction to reinforcement learning and its core models and algorithms. Reinforcement learning is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives while interacting with a complex and uncertain environment. The aim of the course is to give the student the basic ideas and intuition behind modern reinforcement learning methods as well as a bit more formal understanding of how, why, and when they work. Recent progress for deep reinforcement learning and its applications will be discussed

Pre-requisite(s):
AI3053 INTELLIGENT AGENT TECHNOLOGY

Course Description:
This course teaches students the concept and theories of Large-Scale Multi-Agent Systems (LSMAS); the core technologies for the support of LSMAS; the state-of-art LSMAS applications; and the design and development of LSMAS with the adoption of Java Agent Development Environment (JADE) / JaCaMo.

Pre-requisite(s): None 

Course Description:
This course will enable students in Artificial Intelligence Programme to develop competencies expected of professionals working in business, government or the broader community. Students will be assigned to work in a designated AI-related organization such as governmental department, public institution, non-governmental organization, academic and/or research institution, consultancy company, commercial laboratory, or any other organization/company which has implemented AI technology and management. During the intern period, students are expected to apply their professional knowledge gained in the College into a real situation; in addition, students are expected to develop their professional working attitude, ethics, communication skill, team working tactic, and any other specific skills at the host organization in real situation.

* Students can take only one of these ME courses, i.e., the “AI3083 Artificial Intelligence Project” and “AI4043 Artificial Intelligence Internship” courses, which could also be double counted as GE Capstone courses. These courses allow students to gain more practical experience by participating in the research projects of UIC staff or that of our industrial partners. Students will need to identify a teacher who is willing to serve as their project supervisor before registering for the course.

Pre-requisite(s): None 

Course Description:
This course introduces various challenges and opportunities in the Intelligent Internet of things. We discuss topics such as perception and recognitions, RFID and NFC, wireless sensor networks, storage of IoTs, security/privacy of IoTs, and applications. Concepts and state-of-art progress on crowdsensing, passive sensing, and sensor-cloud are also covered.

Pre-requisite(s): None 

Course Description:
This course introduces various challenges and opportunities in 5G Networks and mobile computing. We discuss topics such as wireless communication, 5G Networks, network protocols and standards, ad-hoc networks, location awareness, sensing, application development. Concepts and state-of-art progress in edge computing are also covered as part of the computational model.

Pre-requisite(s):
MATH2003 DISCRETE STRUCTURES

Course Description:
This course aims to introduce the fundamental concepts in theoretical computer science. Topics include deterministic and non-deterministic finite automata, regular languages, context-free languages, Turing machines, Church’s thesis, the halting problem, computability, and complexity. Also, the formal relationships between machines, languages and grammars are addressed.

Nil

Nil

Nil

Pre-requisite(s):
MTH1003 LINEAR ALGEBRA or MTH1053 LINEAR ALGEBRA I, and
MATH 1063 LINEAR ALGEBRA II

Course Description:
Applied Linear Algebra and Dynamics aims to provide some advanced topics and tools related to linear algebra. The course will equipped students with advantages for subsequent courses on data analysis and AI. It consists of orthogonal polynomials, least squares approximation, discrete Fourier analysis and fast Fourier transform, wavelet, positive definite matrices, singular value decomposition, minimum principles, and linear dynamics. It provides solid foundation for compression, optimization theory, principle component analysis, model based data analysis, Markov process and control.

Pre-requisite(s):
MATH1123 CALCULUS FOR SCIENCE AND ENGINEERING, or
MATH1073 CALCULUS I, or
MATH1103 CALCULUS

Course Description:
This course introduces the differential and integral calculus for multivariate functions. Advanced Calculus provides the basics of analytic geometry for lines and planes, curvatures for vector functions, partial derivatives, multiple integrals, infinite sequences and series, and second order differential equations. Advanced Calculus severs the foundations for many advanced courses and is usually a compulsory courses for most Programmes in top graduate schools.

Pre-requisite(s):
MATH1003 LINEAR ALGEBRA or MATH1053 LINEAR ALGEBRA I, and
MATH1073 CALCULUS I or MATH1103 CALCULUS or MATH1123 CALCULUS FOR SCIENCE AND ENGINEERING

Course Description:
This course introduces basic concepts and techniques of measuring theoretic probability. It introduces some some basic stochastic processes, martingales and their applications, and familiarises students with random variable and various probability distributions from the perspective of measuring theoretic probability theory.

Pre-requisite(s): None 

Course Description:
This course teaches the basic principles of physics to explain the properties of heat, light, electricity, magnetism, and quantum mechanics of atoms and then apply the principles to study the functions of electronics, analytical instruments, environmental monitoring instruments, solar panel, etc. In addition, the impacts of important physical phenomena such as air movement, light scattering by particulate matter, global warming, solar radiation, radioactivity, etc. on the formation of environmental risks and pollutions will be analysed. The basic principles of physics taught in this course can be applied not only to Environmental Science, but also to other sciences and everyday life.