Business Applications Relying on Supervised Learning
Dates: 19 Jun, 20 Jun 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 module 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 Ai Xin
Dr Ai Xin
Dr Ai Xin is currently a Lecturer with the School of Computing at the National University of Singapore (NUS). She has many years’ experience on teaching Artificial Intelligence and Data Science courses, e.g. machine learning, deep learning, data mining and etc.
She graduated from NUS with a PhD degree on Electrical and Computer Engineering. Her research focused on Game Theoretical Modelling, Optimization Methods, Algorithm Design and Wireless Networks.
She worked in BHP Billiton Marketing Asia for eight years and gained a lot of industry experience through different functions, e.g. risk management, supply chain management, sales and marketing planning and etc.
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 Code:
TGS-2022015670 (Classroom Learning) / TGS-2022016044 (Synchronous e-learning)
For members of public and NUS Alumnus (without R&G Voucher), please follow the steps below:
Select Short Course / Modular Course -> Apply for Myself -> Browse Academic Modules / Short Courses-> Module/Course Category -> Short Courses -> Browse Courses-> Advanced Computing for Exe (Faculty/Department / Unit)
Please download the user guide for NUS Online Application Portal after you click ‘Apply for Myself’ if you need assistance.
Course Fee Breakdown
Singapore Citizens
39 years old or youngerSingapore Citizen
40 years old or older