Business Applications relying on Unsupervised & Reinforcement Learning

Date: 27 Mar, 28 Mar, 29 Mar 2023 | 9am-5.30pm | Classroom Learning

Duration: 3 Days

Course Overview

Data is the new gold, and skills related to data are highly sought after. In its most common form, Artificial Intelligence and Machine Learning combine a set of techniques that help us to make predictions based on a set of ‘example’ outcomes or labels, given specific inputs. Learning from labelled data examples is called supervised learning. In many cases, however, one cannot rely on labelled data, simply because it is not available. These types of problems are called unsupervised learning problems. Both supervised and unsupervised learning are very relevant and are applicable in almost every industry. One way to deal with unsupervised data is to label the data and convert it to a supervised learning problem. Data labelling, however, can be time-consuming, complex and expensive. In this course, the focus is on native unsupervised learning applications such as Anomaly Detection and Clustering. Clustering groups data with similar characteristics while in Anomaly Detection, the aim is to find data that is considered out of range. Both unsupervised learning applications are very relevant and are used by almost all verticals. Manufacturing companies rely on Anomaly Detection to detect quality issues while banks use it to detect fraudulent transactions.

Recommender Systems used by Amazon, and Netflix are examples of what we call semi-supervised learning applications, because they rely on a small percentage of labelled data. Reinforcement Learning (RL) or learning by doing is also unsupervised and, initially, there are no examples to learn from. The RL agent generates its own examples, over time and adds to the complexity of RL problems. Finally, the course will cover Time Series problems. These types of problems rely on supervised but sequential data.

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:

  • Gain an understanding of popular unsupervised and semi-supervised learning problems including Clustering, Anomaly Detection and Recommender Engines
  • Recognize unsupervised learning problems

Who Should Attend

Data Analyst or roles that require solving data related problems.

Prerequisites

Minimum Diploma and good English knowledge. 

Course Convener

(Click their photos to view their short biographies)

eeeee Mario Favaits

Mr Mario FavaitsMr Mario Favaits

Course Fees

Singapore Citizens
39 years old or younger
40 years old or older
Singapore PRs
Enhanced Training Support for SMEs
International Participants

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-2022015668 (Classroom Learning) / TGS-2022015677 (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

Singapore Citizens

39 years old or younger

Singapore Citizen

40 years old or older
Singapore PRs
Enhanced Training Support for SMEs
International Participants