Pre-requisite(s):
AI1003 PYTHON PROGRAMMING, or
COMP1013 STRUCTURED PROGRAMMING, or
GCIT1013 FOUNDATIONS OF C PROGRAMMING, or
STAT2043 STRUCTURED PROGRAMMING (FOR STAT STUDENTS), or
COMP3153 C++ PROGRAMMING LANGUAGE

Course Description:
This course introduces ways to represent and interpret human languages such as English and Chinese. We discuss applications based on speech data including translation, summarisation, information extraction, question answering and so on. Concepts in machine learning and linguistics are also covered as part of the computational model.

Pre-requisite(s): None

Course Description:
This course provides the student with a solid foundation in the principles of biology, from molecular biology to cells to the diversity of life. Topics include the structure and function of representative organisms, and their diversity. Latest advances in biology are incorporated into the course. There is also an overview of the scientific process/method, and examples are reviewed to show how the process works.

Pre-requisite(s):
MATH1003 LINEAR ALGEBRA, or
MATH1053 LINEAR ALGEBRA I, or
MATH1173 LINEAR ALGEBRA

Course Description:
The aim is to develop solid understanding of the fundamentals of linear systems analysis and design. The focus will be on state space approach though frequency domain techniques and relation between two methods are also covered to some extent.

Pre-requisite(s): None

Course Description:
The aim of this course is to let students experience a complete requirements elicitation process for a Data Science (DS) project. We will learn the Volere Requirement Process, including methods to identify the correct business problem, and to derive & design innovative solutions. We will also learn how to communicate requirements properly using natural language and various modeling techniques such as Unified Modeling Language (UML) and Goal-Oriented Modeling Frameworks.

Pre-requisite(s): None

Course Description:
The aim of this course is to teach students how to create visualisations that effectively communicate the meaning behind data to an observer through visual perception. We will learn how a computer displays information using computer graphics, and how the human perceives that information visually. We will also study the forms of data, including quantitative and non-quantitative data, and how they are properly mapped to the elements of a visualisation to be perceived well by the observer. We will briefly overview some design elements for effective visualisation. The course will also cover with the integration of visualisation into database and data-mining systems to provide support for decision making, and the effective construction of a visualisation dashboard

Pre-requisite(s):
COMP2003 DATA STRUCTURE AND ALGORITHMS, and
DS2013 DATA PROCESSING WORKSHOP I or DS2043 DATA PROCESSING WORKSHOP I

Course Description:
The course is to introduce the latest development of big data analytics and concepts of mining massive datasets. It emphasizes big data analytical techniques which include Finding Similar Items, Mining Data Streams, Link Analysis, Frequent Itemsets, Association rules, Clustering over Massive Datasets, Advertising on the Web, Recommender Systems, Mining Social-Network Graphs, Dimensionality Reduction, and can motivate students to apply big data analytics in addressing problems in real world applications.

Pre-requisite(s):
DS2013 DATA PROCESSING WORKSHOP I or DS2043 DATA PROCESSING WORKSHOP I, and
MATH1003 LINEAR ALGEBRA, and
DS4023 MACHINE LEARNING

Course Description:
This course provides an overview of basic methods to developing state-of-the-art recommender systems. It introduces current algorithms for generating personalized recommendations. It discusses how to measure the effectiveness of recommender systems and illustrates the methods with practical case studies. It also covers emerging topics such as deep learning. It provides basic and state-of-the art technology to build real-world recommender systems. It equips students with some necessary skills for industry or for further study.

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): 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.

Pre-requisite(s):
AI1003 PYTHON PROGRAMMING, or
COMP1013 STRUCTURED PROGRAMMING, or
GCIT1013 FOUNDATIONS OF C PROGRAMMING, or
STAT2043 STRUCTURED PROGRAMMING (FOR STAT STUDENTS), or
COMP3153 C++ PROGRAMMING LANGUAGE

Course Description:
This course introduces ways to represent and interpret human languages such as English and Chinese. We discuss applications based on speech data including translation, summarisation, information extraction, question answering and so on. Concepts in machine learning and linguistics are also covered as part of the computational model.

Pre-requisite(s): None

Course Description:
This course provides the student with a solid foundation in the principles of biology, from molecular biology to cells to the diversity of life. Topics include the structure and function of representative organisms, and their diversity. Latest advances in biology are incorporated into the course. There is also an overview of the scientific process/method, and examples are reviewed to show how the process works.

Pre-requisite(s):
MATH1003 LINEAR ALGEBRA, or
MATH1053 LINEAR ALGEBRA I, or
MATH1173 LINEAR ALGEBRA

Course Description:
The aim is to develop solid understanding of the fundamentals of linear systems analysis and design. The focus will be on state space approach though frequency domain techniques and relation between two methods are also covered to some extent.

Pre-requisite(s): None

Course Description:
The aim of this course is to let students experience a complete requirements elicitation process for a Data Science (DS) project. We will learn the Volere Requirement Process, including methods to identify the correct business problem, and to derive & design innovative solutions. We will also learn how to communicate requirements properly using natural language and various modeling techniques such as Unified Modeling Language (UML) and Goal-Oriented Modeling Frameworks.

Pre-requisite(s): None

Course Description:
The aim of this course is to teach students how to create visualisations that effectively communicate the meaning behind data to an observer through visual perception. We will learn how a computer displays information using computer graphics, and how the human perceives that information visually. We will also study the forms of data, including quantitative and non-quantitative data, and how they are properly mapped to the elements of a visualisation to be perceived well by the observer. We will briefly overview some design elements for effective visualisation. The course will also cover with the integration of visualisation into database and data-mining systems to provide support for decision making, and the effective construction of a visualisation dashboard

Pre-requisite(s):
COMP2003 DATA STRUCTURE AND ALGORITHMS, and
DS2013 DATA PROCESSING WORKSHOP I or DS2043 DATA PROCESSING WORKSHOP I

Course Description:
The course is to introduce the latest development of big data analytics and concepts of mining massive datasets. It emphasizes big data analytical techniques which include Finding Similar Items, Mining Data Streams, Link Analysis, Frequent Itemsets, Association rules, Clustering over Massive Datasets, Advertising on the Web, Recommender Systems, Mining Social-Network Graphs, Dimensionality Reduction, and can motivate students to apply big data analytics in addressing problems in real world applications.

Pre-requisite(s):
DS2013 DATA PROCESSING WORKSHOP I or DS2043 DATA PROCESSING WORKSHOP I, and
MATH1003 LINEAR ALGEBRA, and
DS4023 MACHINE LEARNING

Course Description:
This course provides an overview of basic methods to developing state-of-the-art recommender systems. It introduces current algorithms for generating personalized recommendations. It discusses how to measure the effectiveness of recommender systems and illustrates the methods with practical case studies. It also covers emerging topics such as deep learning. It provides basic and state-of-the art technology to build real-world recommender systems. It equips students with some necessary skills for industry or for further study.

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): 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.

Pre-requisite(s):
AI1003 PYTHON PROGRAMMING, or
COMP1013 STRUCTURED PROGRAMMING, or
GCIT1013 FOUNDATIONS OF C PROGRAMMING, or
STAT2043 STRUCTURED PROGRAMMING (FOR STAT STUDENTS), or
COMP3153 C++ PROGRAMMING LANGUAGE

Course Description:
This course introduces ways to represent and interpret human languages such as English and Chinese. We discuss applications based on speech data including translation, summarisation, information extraction, question answering and so on. Concepts in machine learning and linguistics are also covered as part of the computational model.

Pre-requisite(s): None

Course Description:
This course provides the student with a solid foundation in the principles of biology, from molecular biology to cells to the diversity of life. Topics include the structure and function of representative organisms, and their diversity. Latest advances in biology are incorporated into the course. There is also an overview of the scientific process/method, and examples are reviewed to show how the process works.

Pre-requisite(s):
MATH1003 LINEAR ALGEBRA, or
MATH1053 LINEAR ALGEBRA I, or
MATH1173 LINEAR ALGEBRA

Course Description:
The aim is to develop solid understanding of the fundamentals of linear systems analysis and design. The focus will be on state space approach though frequency domain techniques and relation between two methods are also covered to some extent.

Pre-requisite(s): None

Course Description:
The aim of this course is to let students experience a complete requirements elicitation process for a Data Science (DS) project. We will learn the Volere Requirement Process, including methods to identify the correct business problem, and to derive & design innovative solutions. We will also learn how to communicate requirements properly using natural language and various modeling techniques such as Unified Modeling Language (UML) and Goal-Oriented Modeling Frameworks.

Pre-requisite(s): None

Course Description:
The aim of this course is to teach students how to create visualisations that effectively communicate the meaning behind data to an observer through visual perception. We will learn how a computer displays information using computer graphics, and how the human perceives that information visually. We will also study the forms of data, including quantitative and non-quantitative data, and how they are properly mapped to the elements of a visualisation to be perceived well by the observer. We will briefly overview some design elements for effective visualisation. The course will also cover with the integration of visualisation into database and data-mining systems to provide support for decision making, and the effective construction of a visualisation dashboard

Pre-requisite(s):
COMP2003 DATA STRUCTURE AND ALGORITHMS, and
DS2013 DATA PROCESSING WORKSHOP I or DS2043 DATA PROCESSING WORKSHOP I

Course Description:
The course is to introduce the latest development of big data analytics and concepts of mining massive datasets. It emphasizes big data analytical techniques which include Finding Similar Items, Mining Data Streams, Link Analysis, Frequent Itemsets, Association rules, Clustering over Massive Datasets, Advertising on the Web, Recommender Systems, Mining Social-Network Graphs, Dimensionality Reduction, and can motivate students to apply big data analytics in addressing problems in real world applications.

Pre-requisite(s):
DS2013 DATA PROCESSING WORKSHOP I or DS2043 DATA PROCESSING WORKSHOP I, and
MATH1003 LINEAR ALGEBRA, and
DS4023 MACHINE LEARNING

Course Description:
This course provides an overview of basic methods to developing state-of-the-art recommender systems. It introduces current algorithms for generating personalized recommendations. It discusses how to measure the effectiveness of recommender systems and illustrates the methods with practical case studies. It also covers emerging topics such as deep learning. It provides basic and state-of-the art technology to build real-world recommender systems. It equips students with some necessary skills for industry or for further study.

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): 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.

Pre-requisite(s):
AI1003 PYTHON PROGRAMMING, or
COMP1013 STRUCTURED PROGRAMMING, or
GCIT1013 FOUNDATIONS OF C PROGRAMMING, or
STAT2043 STRUCTURED PROGRAMMING (FOR STAT STUDENTS), or
COMP3153 C++ PROGRAMMING LANGUAGE

Course Description:
This course introduces ways to represent and interpret human languages such as English and Chinese. We discuss applications based on speech data including translation, summarisation, information extraction, question answering and so on. Concepts in machine learning and linguistics are also covered as part of the computational model.

Pre-requisite(s): None

Course Description:
This course provides the student with a solid foundation in the principles of biology, from molecular biology to cells to the diversity of life. Topics include the structure and function of representative organisms, and their diversity. Latest advances in biology are incorporated into the course. There is also an overview of the scientific process/method, and examples are reviewed to show how the process works.

Pre-requisite(s):
MATH1003 LINEAR ALGEBRA, or
MATH1053 LINEAR ALGEBRA I, or
MATH1173 LINEAR ALGEBRA

Course Description:
The aim is to develop solid understanding of the fundamentals of linear systems analysis and design. The focus will be on state space approach though frequency domain techniques and relation between two methods are also covered to some extent.

Pre-requisite(s): None

Course Description:
The aim of this course is to let students experience a complete requirements elicitation process for a Data Science (DS) project. We will learn the Volere Requirement Process, including methods to identify the correct business problem, and to derive & design innovative solutions. We will also learn how to communicate requirements properly using natural language and various modeling techniques such as Unified Modeling Language (UML) and Goal-Oriented Modeling Frameworks.

Pre-requisite(s): None

Course Description:
The aim of this course is to teach students how to create visualisations that effectively communicate the meaning behind data to an observer through visual perception. We will learn how a computer displays information using computer graphics, and how the human perceives that information visually. We will also study the forms of data, including quantitative and non-quantitative data, and how they are properly mapped to the elements of a visualisation to be perceived well by the observer. We will briefly overview some design elements for effective visualisation. The course will also cover with the integration of visualisation into database and data-mining systems to provide support for decision making, and the effective construction of a visualisation dashboard

Pre-requisite(s):
COMP2003 DATA STRUCTURE AND ALGORITHMS, and
DS2013 DATA PROCESSING WORKSHOP I or DS2043 DATA PROCESSING WORKSHOP I

Course Description:
The course is to introduce the latest development of big data analytics and concepts of mining massive datasets. It emphasizes big data analytical techniques which include Finding Similar Items, Mining Data Streams, Link Analysis, Frequent Itemsets, Association rules, Clustering over Massive Datasets, Advertising on the Web, Recommender Systems, Mining Social-Network Graphs, Dimensionality Reduction, and can motivate students to apply big data analytics in addressing problems in real world applications.

Pre-requisite(s):
DS2013 DATA PROCESSING WORKSHOP I or DS2043 DATA PROCESSING WORKSHOP I, and
MATH1003 LINEAR ALGEBRA, and
DS4023 MACHINE LEARNING

Course Description:
This course provides an overview of basic methods to developing state-of-the-art recommender systems. It introduces current algorithms for generating personalized recommendations. It discusses how to measure the effectiveness of recommender systems and illustrates the methods with practical case studies. It also covers emerging topics such as deep learning. It provides basic and state-of-the art technology to build real-world recommender systems. It equips students with some necessary skills for industry or for further study.

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): 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.