Singapore University of Social Sciences

Big Data, Cloud Computing and Machine Learning (FIN559)

Applications Open: 01 October 2019

Applications Close: 30 November 2019

Next Available Intake: January 2020

Course Types: Modular Graduate Course, SkillsFuture Series

Language: English

Duration: 6 months

Fees: To be confirmed

Area of Interest: Finance

Schemes: Lifelong Learning Credit (L2C)

Funding: SkillsFuture


Synopsis

FIN559 Big Data, Cloud Computing and Machine learning provides an overview of computing and cloud technology that brings disruptive changes to various businesses including financial services. It starts with providing a foundational understanding of big data processing framework and approaches, and follows by introducing relevant tools to students through case studies and hands-on training. The course then examines the core elements of cloud computing, the different types of clouds, and the risk and security issues in cloud computing. It provides a framework for selecting the optimal combination of cloud technology to meet business requirements. Furthermore, the course introduces machine learning, covering both supervised and unsupervised algorithms. It discusses biasvariance trade-off for selecting the appropriate models and introduces neural networks and convolutional neural networks. Lastly the course discusses the applications of artificial intelligence and machine learning via case studies.

Level: 5
Credit Units: 5
Presentation Pattern: Every January

Topics

  • Challenges of big-data computing (the 5 V’s of big data)
  • Big-data computing requirements and framework
  • Programming tool and API for big-data computing
  • Integrating big-data computing in smart solutions
  • Core elements of cloud computing and different types of clouds
  • Security and privacy threats in cloud computing
  • Risk management plan for cloud computing
  • Technical and business models of cloud computing: IaaS, PaaS and SaaS
  • Implementing cloud computing using open source software
  • Introduction to machine learning
  • Supervised learning and unsupervised learning
  • Bias-variance tradeoff
  • Neural networks and convolutional neural networks
  • Artificial intelligence and machine learning applications and case studies

Learning Outcome

  • Examine the challenges in big data computing and evaluate the big-data computing framework
  • Appraise approaches to resolve big-data computing issues
  • Assess core elements of cloud computing and different types of clouds
  • Appraise risk management plans for cloud computing
  • Evaluate IaaS, PaaS and SaaS technology
  • Assess supervised learning and unsupervised learning
  • Evaluate neural networks and convolutional neural networks
  • Construct smart solution based on big-data computing
  • Formulate calculation for practicing cloud economics
  • Implement cloud computing using open source software
  • Design and develop machine learning applications
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