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Short Course

Business Applications Relying on Supervised Learning

Programme ScheduleDuration (days)Mode of DeliveryProgramme Code
To be advised2Classroom LearningTGS-2022015670

Course Overview

This course helps learners understand how to apply supervised learning techniques to business applications. Topics such as different supervised learning techniques, business applications and case studies, build and evaluate the predictive models using real-life business datasets will be covered.

Course Objectives

At the end of this course, learners will be able to:

  • Understand several useful supervised learning techniques, e.g. decision tree, linear regression and neural networks
  • Apply supervised learning techniques to solve real-life business problems, e.g. fraud detection and regression analysis
  • Identify the business problem, build supervised learning models, compare the model performance and finalise the business solution

Who Should Attend

Executives, Developers, Designers and Managers in Information Technology related fields, business development, strategic planning and operations.

Prerequisites

  • Knowledge of Rapidminer
  • Minimum Diploma in IT or related fields
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This programme is part of

  • Professional Certificate in Machine Learning for Business

    • Types of Available Fundings

  • SkillsFuture Credit
  • UTAP
  • PSEA
  • Enhanced Training Support
  • SFEC

    • Instructors


    Dr Natarajan Prabhu

    Dr Natarajan Prabhu

    Dr Natarajan Prabhu is currently a senior lecturer in the School of Computing at the National University of Singapore. He has 10+ years of experience in teaching for master’s degree programs, undergraduate modules, and continuing education courses. Before joining NUS, he was teaching at DigiPen Institute of Technology, where he taught AI for Games, Digital Image Processing, Machine Learning, Deep Learning, Data Structures, etc. In DigiPen, he developed a master’ degree program for Computer Vision that primarily prepares graduate students to work in the CV industry. After joining NUS as a lecturer, he is currently working on developing and teaching an AI module for non-CS students in Blended learning. He graduated with a Ph.D. degree from NUS in 2013, a master’s degree, and a bachelor’s degree from Anna University in 2008 and 2006, respectively. His Ph.D. thesis was about automatically controlling and coordinating multiple active cameras in surveillance networks. During this time he has gained rich experience in building multi-camera surveillance systems. He has received “Best PhD Forum Paper” award from International Conference on Distributed Smart Cameras (Hong Kong, 2012) and “Research Achievement Award” from School of Computing, NUS (2012).

    Programme Fees


    Singapore Citizens Singapore PRs Enhanced Training Support for SMEs International Participants
    39 years old
    or younger    		 40 years old
    or older
    Full Programme Fee S$2,100.00 S$2,100.00 S$2,100.00 S$2,100.00 S$2,100.00
    Less: SSG Grant Amount S$1,470.00 S$1,470.00 S$1,470.00 S$1,470.00 -
    Nett Programme Fee S$630.00 S$630.00 S$630.00 S$630.00 S$2,100.00
    9% GST on Nett Programme Fee S$56.70 S$56.70 S$56.70 S$56.70 S$189.00
    Total Nett Programme Fee Payable, Including GST S$686.70 S$686.70 S$686.70 S$686.70 S$2,289.00
    Less Additional Funding - S$420.00 - S$420.00 -
    Total Nett Programme Fee Payable, Including GST, after additional funding from the various funding schemes S$686.70 S$266.70 S$686.70 S$266.70 S$2,289.00
    1. Total Nett Programme Fee Payable, including GST, after additional funding from various funding schemes.
    2. GST shall apply at prevailing rates.
    3. Participants must fulfill at least 75% attendance and pass all assessment components to be eligible for SSG funding.

    Other Information

    • This course is eligible for Union Training Assistance Programme (UTAP). NTUC members can enjoy up to 50% funding (capped at $250 per year) under UTAP. NTUC members aged 40 and above can enjoy higher funding support up to $500 per individual each year, capped at 50% of unfunded course fees, for courses attended between 1 July 2020 to 31 December 2025. Please click here for more information.
    • Learn more about supervised learning in our article, “Demystifying Supervised Learning: A Beginner’s Guide”. Or, download a PDF copy.

    Connect with us

    11 Research Link
    COM 3, #B1-07
    Singapore 119391
    Telephone: +65 6516 5523
    Email: soc-ace@nus.edu.sg

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