Pre-requisite(s): None
Course Description:
This course is a survey of the introductory
concepts and techniques of game-theoretic analysis and their
applications. It offers a non-technical exposure to game theory with a
special emphasis on examples and applications drawn from science, economics, political and other fields in social sciences. As such, the
course focuses on the identification and analysis of archetypal
strategic situations frequently encountered in real-life experiences.
Pre-requisite(s): None
Course Description:
This course is a survey of the introductory
concepts and techniques of game-theoretic analysis and their
applications. It offers a non-technical exposure to game theory with a
special emphasis on examples and applications drawn from science, economics, political and other fields in social sciences. As such, the
course focuses on the identification and analysis of archetypal
strategic situations frequently encountered in real-life experiences.
Pre-requisite(s): None
Course Description:
This course is a survey of the introductory
concepts and techniques of game-theoretic analysis and their
applications. It offers a non-technical exposure to game theory with a
special emphasis on examples and applications drawn from science, economics, political and other fields in social sciences. As such, the
course focuses on the identification and analysis of archetypal
strategic situations frequently encountered in real-life experiences.
Pre-requisite(s):
AI2033 PROBABILITY AND STATISTICS
Course Description:
Decision theory studies the logic and the
mathematical properties of decision making under uncertainty.
Statistical decision theory focuses on the investigation of decision
making when uncertainty can be reduced by information acquired
through experimentation. This course is a survey of the introductory
concepts and techniques of decision theory and their applications
from a statistical perspective. The objective is to introduce many
basic concepts and methods of decision making to the students.
Pre-requisite(s):
MATH1003 LINEAR ALGEBRA, or
MATH1063 LINEAR ALGEBRA II
Course Description:
This course is designed to
introduce theory of regression analysis and techniques which have
been used in data analysis; to emphasise recent developments in the
regression analysis such as statistical diagnostics and nonlinear
regression, and motivate students to analyse multivariate data with
the help of statistical packages such as MATLAB, R or SPSS.
Pre-requisite(s): None
Course Description:
This course teaches students the basic theory
of Fintech and its underlying technologies including: Basic
Cryptography, Cryptocurrency, Blockchain Technology, Digital
Ledgers, Robo Advisor, etc. It also teaches students the usage of
contemporary Fintech development tools, real-time intelligent
financial system development tools, and software packages, and how
to apply Fintech and related technologies to develop intelligent
financial and banking systems.
Pre-requisite(s):
MATH 1003 LINEAR ALGEBRA, and
AI2033 PROBABILITY AND STATISTICS
Course Description:
This course aims to equip students with
knowledge and skills to design machines or classifiers than can
extract features and recognize patterns - understanding the basic
science, arts, algorithms, and technologies of pattern recognition.
The course focuses on mapping various real world problems into
pattern recognition frameworks, studying statistical pattern
recognition approaches, and experimenting with real world problems
to appreciate the methodologies of pattern analytics.
Pre-requisite(s):
AI1023 DATABASE MANAGEMENT SYSTEMS, or
COMP3013 DATABASE MANAGEMENT SYSTEMS, or
EBIS3003 DATABASE MANAGEMENT
Course Description:
This course provides an overview of the
concepts and techniques in knowledge discovery and data mining.
The students are expected to have some ideas about some basic
knowledge discovery and data mining techniques, including
classification, clustering, data association and data warehouse.
Pre-requisite(s):
COMP1013 STRUCTURED PROGRAMMING, or
GCIT1013 FOUNDATIONS OF C PROGRAMMING, or
AI1013 OBJECT-ORIENTED PROGRAMMING, or
COMP2013 OBJECT-ORIENTED PROGRAMMING, or
STAT2043 STRUCTURED PROGRAMMING (FOR STAT STUDENTS), or
COMP3153 C++ PROGRAMMING LANGUAGE
Course Description:
With the exponential growth of program trading in worldwide financial industry, Quantum Finance and its underlying technologies including quantum field theory and chaos theory become one of the hottest topics in the Fintech community. Many worldwide financial institutions and fund houses have the needs to recruit computer professionals with basic knowledge on quantum finance to develop intelligent financial systems. The objective of this course is to teach students the basic knowledge of quantum finance and its underlying theories and technologies including quantum field theory, chaos theory and chaotic neural networks and how to apply these technologies to finance industry to develop intelligent financial prediction and trading systems.
Pre-requisite(s):
MATH1063 LINEAR ALGEBRA II, or
MATH1003 LINEAR ALGEBRA
Course Description:
This course provides an understanding of classical multivariate analysis and modern techniques in data mining which are useful for analysing both designed experiments and observational studies. Real data in social, life, and natural sciences are analysed using statistical packages such as R or MATLAB.
Pre-requisite(s): None
Course Description:
This course is a survey of the introductory
concepts and techniques of game-theoretic analysis and their
applications. It offers a non-technical exposure to game theory with a
special emphasis on examples and applications drawn from science, economics, political and other fields in social sciences. As such, the
course focuses on the identification and analysis of archetypal
strategic situations frequently encountered in real-life experiences.
Pre-requisite(s): None
Course Description:
This course is a survey of the introductory
concepts and techniques of game-theoretic analysis and their
applications. It offers a non-technical exposure to game theory with a
special emphasis on examples and applications drawn from science, economics, political and other fields in social sciences. As such, the
course focuses on the identification and analysis of archetypal
strategic situations frequently encountered in real-life experiences.
Pre-requisite(s):
AI2033 PROBABILITY AND STATISTICS
Course Description:
Decision theory studies the logic and the
mathematical properties of decision making under uncertainty.
Statistical decision theory focuses on the investigation of decision
making when uncertainty can be reduced by information acquired
through experimentation. This course is a survey of the introductory
concepts and techniques of decision theory and their applications
from a statistical perspective. The objective is to introduce many
basic concepts and methods of decision making to the students.
Pre-requisite(s):
MATH1003 LINEAR ALGEBRA, or
MATH1063 LINEAR ALGEBRA II
Course Description:
This course is designed to
introduce theory of regression analysis and techniques which have
been used in data analysis; to emphasise recent developments in the
regression analysis such as statistical diagnostics and nonlinear
regression, and motivate students to analyse multivariate data with
the help of statistical packages such as MATLAB, R or SPSS.
Pre-requisite(s): None
Course Description:
This course teaches students the basic theory
of Fintech and its underlying technologies including: Basic
Cryptography, Cryptocurrency, Blockchain Technology, Digital
Ledgers, Robo Advisor, etc. It also teaches students the usage of
contemporary Fintech development tools, real-time intelligent
financial system development tools, and software packages, and how
to apply Fintech and related technologies to develop intelligent
financial and banking systems.
Pre-requisite(s):
MATH 1003 LINEAR ALGEBRA, and
AI2033 PROBABILITY AND STATISTICS
Course Description:
This course aims to equip students with
knowledge and skills to design machines or classifiers than can
extract features and recognize patterns - understanding the basic
science, arts, algorithms, and technologies of pattern recognition.
The course focuses on mapping various real world problems into
pattern recognition frameworks, studying statistical pattern
recognition approaches, and experimenting with real world problems
to appreciate the methodologies of pattern analytics.
Pre-requisite(s):
AI1023 DATABASE MANAGEMENT SYSTEMS, or
COMP3013 DATABASE MANAGEMENT SYSTEMS, or
EBIS3003 DATABASE MANAGEMENT
Course Description:
This course provides an overview of the
concepts and techniques in knowledge discovery and data mining.
The students are expected to have some ideas about some basic
knowledge discovery and data mining techniques, including
classification, clustering, data association and data warehouse.
Pre-requisite(s):
COMP1013 STRUCTURED PROGRAMMING, or
GCIT1013 FOUNDATIONS OF C PROGRAMMING, or
AI1013 OBJECT-ORIENTED PROGRAMMING, or
COMP2013 OBJECT-ORIENTED PROGRAMMING, or
STAT2043 STRUCTURED PROGRAMMING (FOR STAT STUDENTS), or
COMP3153 C++ PROGRAMMING LANGUAGE
Course Description:
With the exponential growth of program trading in worldwide financial industry, Quantum Finance and its underlying technologies including quantum field theory and chaos theory become one of the hottest topics in the Fintech community. Many worldwide financial institutions and fund houses have the needs to recruit computer professionals with basic knowledge on quantum finance to develop intelligent financial systems. The objective of this course is to teach students the basic knowledge of quantum finance and its underlying theories and technologies including quantum field theory, chaos theory and chaotic neural networks and how to apply these technologies to finance industry to develop intelligent financial prediction and trading systems.
Pre-requisite(s):
MATH1063 LINEAR ALGEBRA II, or
MATH1003 LINEAR ALGEBRA
Course Description:
This course provides an understanding of classical multivariate analysis and modern techniques in data mining which are useful for analysing both designed experiments and observational studies. Real data in social, life, and natural sciences are analysed using statistical packages such as R or MATLAB.
Pre-requisite(s): None
Course Description:
This course is a survey of the introductory
concepts and techniques of game-theoretic analysis and their
applications. It offers a non-technical exposure to game theory with a
special emphasis on examples and applications drawn from science, economics, political and other fields in social sciences. As such, the
course focuses on the identification and analysis of archetypal
strategic situations frequently encountered in real-life experiences.
Pre-requisite(s): None
Course Description:
This course is a survey of the introductory
concepts and techniques of game-theoretic analysis and their
applications. It offers a non-technical exposure to game theory with a
special emphasis on examples and applications drawn from science, economics, political and other fields in social sciences. As such, the
course focuses on the identification and analysis of archetypal
strategic situations frequently encountered in real-life experiences.
Pre-requisite(s):
AI2033 PROBABILITY AND STATISTICS
Course Description:
Decision theory studies the logic and the
mathematical properties of decision making under uncertainty.
Statistical decision theory focuses on the investigation of decision
making when uncertainty can be reduced by information acquired
through experimentation. This course is a survey of the introductory
concepts and techniques of decision theory and their applications
from a statistical perspective. The objective is to introduce many
basic concepts and methods of decision making to the students.
Pre-requisite(s):
MATH1003 LINEAR ALGEBRA, or
MATH1063 LINEAR ALGEBRA II
Course Description:
This course is designed to
introduce theory of regression analysis and techniques which have
been used in data analysis; to emphasise recent developments in the
regression analysis such as statistical diagnostics and nonlinear
regression, and motivate students to analyse multivariate data with
the help of statistical packages such as MATLAB, R or SPSS.
Pre-requisite(s): None
Course Description:
This course teaches students the basic theory
of Fintech and its underlying technologies including: Basic
Cryptography, Cryptocurrency, Blockchain Technology, Digital
Ledgers, Robo Advisor, etc. It also teaches students the usage of
contemporary Fintech development tools, real-time intelligent
financial system development tools, and software packages, and how
to apply Fintech and related technologies to develop intelligent
financial and banking systems.
Pre-requisite(s):
MATH 1003 LINEAR ALGEBRA, and
AI2033 PROBABILITY AND STATISTICS
Course Description:
This course aims to equip students with
knowledge and skills to design machines or classifiers than can
extract features and recognize patterns - understanding the basic
science, arts, algorithms, and technologies of pattern recognition.
The course focuses on mapping various real world problems into
pattern recognition frameworks, studying statistical pattern
recognition approaches, and experimenting with real world problems
to appreciate the methodologies of pattern analytics.
Pre-requisite(s):
AI1023 DATABASE MANAGEMENT SYSTEMS, or
COMP3013 DATABASE MANAGEMENT SYSTEMS, or
EBIS3003 DATABASE MANAGEMENT
Course Description:
This course provides an overview of the
concepts and techniques in knowledge discovery and data mining.
The students are expected to have some ideas about some basic
knowledge discovery and data mining techniques, including
classification, clustering, data association and data warehouse.
Pre-requisite(s):
COMP1013 STRUCTURED PROGRAMMING, or
GCIT1013 FOUNDATIONS OF C PROGRAMMING, or
AI1013 OBJECT-ORIENTED PROGRAMMING, or
COMP2013 OBJECT-ORIENTED PROGRAMMING, or
STAT2043 STRUCTURED PROGRAMMING (FOR STAT STUDENTS), or
COMP3153 C++ PROGRAMMING LANGUAGE
Course Description:
With the exponential growth of program trading in worldwide financial industry, Quantum Finance and its underlying technologies including quantum field theory and chaos theory become one of the hottest topics in the Fintech community. Many worldwide financial institutions and fund houses have the needs to recruit computer professionals with basic knowledge on quantum finance to develop intelligent financial systems. The objective of this course is to teach students the basic knowledge of quantum finance and its underlying theories and technologies including quantum field theory, chaos theory and chaotic neural networks and how to apply these technologies to finance industry to develop intelligent financial prediction and trading systems.
Pre-requisite(s):
MATH1063 LINEAR ALGEBRA II, or
MATH1003 LINEAR ALGEBRA
Course Description:
This course provides an understanding of classical multivariate analysis and modern techniques in data mining which are useful for analysing both designed experiments and observational studies. Real data in social, life, and natural sciences are analysed using statistical packages such as R or MATLAB.
Pre-requisite(s): None
Course Description:
This course is a survey of the introductory
concepts and techniques of game-theoretic analysis and their
applications. It offers a non-technical exposure to game theory with a
special emphasis on examples and applications drawn from science, economics, political and other fields in social sciences. As such, the
course focuses on the identification and analysis of archetypal
strategic situations frequently encountered in real-life experiences.
Pre-requisite(s): None
Course Description:
This course is a survey of the introductory
concepts and techniques of game-theoretic analysis and their
applications. It offers a non-technical exposure to game theory with a
special emphasis on examples and applications drawn from science, economics, political and other fields in social sciences. As such, the
course focuses on the identification and analysis of archetypal
strategic situations frequently encountered in real-life experiences.
Pre-requisite(s):
AI2033 PROBABILITY AND STATISTICS
Course Description:
Decision theory studies the logic and the
mathematical properties of decision making under uncertainty.
Statistical decision theory focuses on the investigation of decision
making when uncertainty can be reduced by information acquired
through experimentation. This course is a survey of the introductory
concepts and techniques of decision theory and their applications
from a statistical perspective. The objective is to introduce many
basic concepts and methods of decision making to the students.
Pre-requisite(s):
MATH1003 LINEAR ALGEBRA, or
MATH1063 LINEAR ALGEBRA II
Course Description:
This course is designed to
introduce theory of regression analysis and techniques which have
been used in data analysis; to emphasise recent developments in the
regression analysis such as statistical diagnostics and nonlinear
regression, and motivate students to analyse multivariate data with
the help of statistical packages such as MATLAB, R or SPSS.
Pre-requisite(s): None
Course Description:
This course teaches students the basic theory
of Fintech and its underlying technologies including: Basic
Cryptography, Cryptocurrency, Blockchain Technology, Digital
Ledgers, Robo Advisor, etc. It also teaches students the usage of
contemporary Fintech development tools, real-time intelligent
financial system development tools, and software packages, and how
to apply Fintech and related technologies to develop intelligent
financial and banking systems.
Pre-requisite(s):
MATH 1003 LINEAR ALGEBRA, and
AI2033 PROBABILITY AND STATISTICS
Course Description:
This course aims to equip students with
knowledge and skills to design machines or classifiers than can
extract features and recognize patterns - understanding the basic
science, arts, algorithms, and technologies of pattern recognition.
The course focuses on mapping various real world problems into
pattern recognition frameworks, studying statistical pattern
recognition approaches, and experimenting with real world problems
to appreciate the methodologies of pattern analytics.
Pre-requisite(s):
AI1023 DATABASE MANAGEMENT SYSTEMS, or
COMP3013 DATABASE MANAGEMENT SYSTEMS, or
EBIS3003 DATABASE MANAGEMENT
Course Description:
This course provides an overview of the
concepts and techniques in knowledge discovery and data mining.
The students are expected to have some ideas about some basic
knowledge discovery and data mining techniques, including
classification, clustering, data association and data warehouse.
Pre-requisite(s):
COMP1013 STRUCTURED PROGRAMMING, or
GCIT1013 FOUNDATIONS OF C PROGRAMMING, or
AI1013 OBJECT-ORIENTED PROGRAMMING, or
COMP2013 OBJECT-ORIENTED PROGRAMMING, or
STAT2043 STRUCTURED PROGRAMMING (FOR STAT STUDENTS), or
COMP3153 C++ PROGRAMMING LANGUAGE
Course Description:
With the exponential growth of program trading in worldwide financial industry, Quantum Finance and its underlying technologies including quantum field theory and chaos theory become one of the hottest topics in the Fintech community. Many worldwide financial institutions and fund houses have the needs to recruit computer professionals with basic knowledge on quantum finance to develop intelligent financial systems. The objective of this course is to teach students the basic knowledge of quantum finance and its underlying theories and technologies including quantum field theory, chaos theory and chaotic neural networks and how to apply these technologies to finance industry to develop intelligent financial prediction and trading systems.
Pre-requisite(s):
MATH1063 LINEAR ALGEBRA II, or
MATH1003 LINEAR ALGEBRA
Course Description:
This course provides an understanding of classical multivariate analysis and modern techniques in data mining which are useful for analysing both designed experiments and observational studies. Real data in social, life, and natural sciences are analysed using statistical packages such as R or MATLAB.
Pre-requisite(s): None
Course Description:
This course is a survey of the introductory
concepts and techniques of game-theoretic analysis and their
applications. It offers a non-technical exposure to game theory with a
special emphasis on examples and applications drawn from science, economics, political and other fields in social sciences. As such, the
course focuses on the identification and analysis of archetypal
strategic situations frequently encountered in real-life experiences.
Pre-requisite(s):
AI2033 PROBABILITY AND STATISTICS
Course Description:
Decision theory studies the logic and the
mathematical properties of decision making under uncertainty.
Statistical decision theory focuses on the investigation of decision
making when uncertainty can be reduced by information acquired
through experimentation. This course is a survey of the introductory
concepts and techniques of decision theory and their applications
from a statistical perspective. The objective is to introduce many
basic concepts and methods of decision making to the students.
Pre-requisite(s):
MATH1003 LINEAR ALGEBRA, or
MATH1063 LINEAR ALGEBRA II
Course Description:
This course is designed to
introduce theory of regression analysis and techniques which have
been used in data analysis; to emphasise recent developments in the
regression analysis such as statistical diagnostics and nonlinear
regression, and motivate students to analyse multivariate data with
the help of statistical packages such as MATLAB, R or SPSS.
Pre-requisite(s): None
Course Description:
This course teaches students the basic theory
of Fintech and its underlying technologies including: Basic
Cryptography, Cryptocurrency, Blockchain Technology, Digital
Ledgers, Robo Advisor, etc. It also teaches students the usage of
contemporary Fintech development tools, real-time intelligent
financial system development tools, and software packages, and how
to apply Fintech and related technologies to develop intelligent
financial and banking systems.
Pre-requisite(s):
MATH 1003 LINEAR ALGEBRA, and
AI2033 PROBABILITY AND STATISTICS
Course Description:
This course aims to equip students with
knowledge and skills to design machines or classifiers than can
extract features and recognize patterns - understanding the basic
science, arts, algorithms, and technologies of pattern recognition.
The course focuses on mapping various real world problems into
pattern recognition frameworks, studying statistical pattern
recognition approaches, and experimenting with real world problems
to appreciate the methodologies of pattern analytics.
Pre-requisite(s):
AI1023 DATABASE MANAGEMENT SYSTEMS, or
COMP3013 DATABASE MANAGEMENT SYSTEMS, or
EBIS3003 DATABASE MANAGEMENT
Course Description:
This course provides an overview of the
concepts and techniques in knowledge discovery and data mining.
The students are expected to have some ideas about some basic
knowledge discovery and data mining techniques, including
classification, clustering, data association and data warehouse.
Pre-requisite(s):
COMP1013 STRUCTURED PROGRAMMING, or
GCIT1013 FOUNDATIONS OF C PROGRAMMING, or
AI1013 OBJECT-ORIENTED PROGRAMMING, or
COMP2013 OBJECT-ORIENTED PROGRAMMING, or
STAT2043 STRUCTURED PROGRAMMING (FOR STAT STUDENTS), or
COMP3153 C++ PROGRAMMING LANGUAGE
Course Description:
With the exponential growth of program trading in worldwide financial industry, Quantum Finance and its underlying technologies including quantum field theory and chaos theory become one of the hottest topics in the Fintech community. Many worldwide financial institutions and fund houses have the needs to recruit computer professionals with basic knowledge on quantum finance to develop intelligent financial systems. The objective of this course is to teach students the basic knowledge of quantum finance and its underlying theories and technologies including quantum field theory, chaos theory and chaotic neural networks and how to apply these technologies to finance industry to develop intelligent financial prediction and trading systems.
Pre-requisite(s):
MATH1063 LINEAR ALGEBRA II, or
MATH1003 LINEAR ALGEBRA
Course Description:
This course provides an understanding of classical multivariate analysis and modern techniques in data mining which are useful for analysing both designed experiments and observational studies. Real data in social, life, and natural sciences are analysed using statistical packages such as R or MATLAB.
