Business Applications Relying on Supervised Learning
Dates: 30 Oct, 31 Oct 2023 | 9am – 5.30pm | Classroom Learning
Duration: 2 Days
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.
This course is part of Professional Certificate in Machine Learning for Business.
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
Minimum Diploma in IT or related fields.
Course Convener
(Click their photos to view their short biographies)
Dr Natarajan Prabhu
Dr Natarajan Prabhu
Dr Natarajan Prabhu is currently a 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).
Course Fees
Total Nett Programme Fee Payable, Including GST, after additional funding from the various funding schemes
Participants must fulfill at least 75% attendance and pass all assessment components to be eligible for SSG funding.
To enquire, email soc-ace@nus.edu.sg
To register, click Register
Course Codes
TGS-2022015670 (Classroom Learning) / TGS-2022016044 (Synchronous e-learning)
Course Fee Breakdown
Singapore Citizens
39 years old or youngerSingapore Citizen
40 years old or older