Graduate Courses in Mathematics
- PhD and MSc by Research
- MSc in Analytics
- MSc in Financial Technology
Courses for PhD and MSc by Research
Advanced courses in mathematics, covering core topics tested in the PhD Qualifying Examinations.
Abstract integration (basic topology, general Lebesgue-like integrals and measures); positive Borel measures (Riesz representation theorem for positive linear functionals); Lp-spaces; integration on product spaces; abstract differentiation; holomorphic functions.
Enumeration; graph and network algorithms; finite fields and applications; boolean algebras; polyhedra and linear programming; algorithmic complexity.
Groups, rings, and fields; basic techniques of group theory; Galois theory.
Review of probability, random variables and their distributions, moments and inequalities; point estimation in parametric setting; point estimation in nonparametric setting; interval estimation and hypothesis testing.
Turing machines; time complexity and space complexity; algorithm design and analysis (greedy, divide and conquer, dynamic programming); graph algorithms; network flow.
Specialized courses offered based on student and lecturer interest. The precise course contents are subject to variation.
Seminars on new research developments in the Mathematical Sciences.
Seminar course in scientific computing.
Courses for MSc in Analytics
Courses for MSc in Financial Technology
This course covers essential machine learning techniques in finance. The emphasis is placed on the financial applications and how can they transform the finance industry. This course will cover supervised learning, unsupervised learning, and deep learning. This course will also train the students’ soft skills through the group project on realistic data analysis problem.
Basic foundation needed to understand the modeling of uncertain phenomena.
This course gives an overview of all the changes, which are happening now in the financial industry and discusses how some of the FinTech processes are being constructed. Each FinTech disruption concept is based on a mathematical of behaviour concept, which is backed by data, analysis and technology. This course goes into detail into some of these processes, so give an understanding as to what is the business model, skill, and future of FinTech in the financial services industry. It will also cover the recent progresses on FinTech development and applications. Although the topics may vary in order to keep pace with the FinTech development, they mainly involve case studies, practical challenges, trends, and opportunities in a FinTech career.
This course discusses the existing and future landscapes of FinTech in Singapore, from incumbent financial firms to FinTech startups. Both traditional and new players are working with policy-makers to define the ecosystem, to encourage innovation, adoption while maintaining regulatory oversight.
This course provides an introduction to the basic principles and theory of finance, terminology and commonly used tools. The course will specifically discuss the financial system, financial statements and financial statement analysis, time value of money, basic valuation of bonds and stocks, capital budgeting processes and techniques, and risk analysis
Prescribed Electives Course for Intelligent Automation Specialisation
In this course, students will learn state-of-the-art deep learning methods for Natural language processing (NLP). Through lectures, practical assignments and projects, students will learn the necessary tricks for making their deep learning models work on practical problems. They will learn to implement, and possibly to invent their own deep learning models using available deep learning libraries.
This is an introductory course that attempts to answer the following questions: What is blockchain? What does blockchain aim to achieve? What are the useful properties of blockchains? What are the building blocks of blockchain? What are the design principles underlying the building blocks of blockchain? What are the use cases for blockchains? What is cryptoasset and cryptocurrency? How to evaluate cryptoasset/cryptocurrency? What is Bitcoin? What is the relationship between Bitcoin and blockchain?
This course builds upon the basic blockchain knowledge discussed in the introductory course to understand the most popular blockchain networks: Ethereum. It covers the mechanics of Ethereum and how it aims to become a global computer through its artifact smart contracts. We will learn one of the languages for smart contract: Solidity and use this to code smart contracts. With these tools, we explore the processes and principles of building decentralized apps on the Ethereum platform.
This course covers the quantitative methods to construct computer-based algorithms for automatic trading and asset management. A number of notable algorithmic trading strategies are discussed. This course also emphasizes the rationale behind the winning strategies, backtesting, automated execution and how to build robots for trading and asset management with specific goals. Moreover, the course provides a hands-on experience of implementing the financial solutions with real market data.
Prescribed Elective Courses for Digital Services Specialisation
This course explores management, organizational, and technological issues in the ways data are stored, managed and applied in businesses. Using a simulated business, the database module covers data concepts, structures, conceptual and physical design techniques, data administration and data mining. Theory and practice of database management systems are integrated through hands-on experience with the design and implementation of a business solution. By the end of the course, participants will gain critical IT skills in analysing business processes, improving these processes, developing business applications with an industry standard database and use data for business requirements.
Unrestricted Elective Courses
This course covers basic and essential quantitative methods in finance. A number of mathematical and statistical techniques are introduced. This course emphasizes the applications of the quantitative methods in two important areas in finance: asset management and derivative pricing.