Business Applications Relying on Unsupervised & Reinforcement Learning

Short Course

Business Applications Relying on Unsupervised & Reinforcement Learning

Programme Schedule	Duration (days)	Mode of Delivery	Programme Code
To be advised	3	Classroom Learning	TGS-2022015668

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.

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.
• Recognise unsupervised learning problems
• Solve real business problems, including customer segmentation, quality control and fraud detection use cases, using RapidMiner.
• Understand bias and that some supervised classification problems are better solved via Anomaly Detection.
• Make a dataset ‘Artificial Intelligence-ready’ using RapidMiner.
• Solve real business problems associated with time series or sequential data using RapidMiner.
• Understand the complexities of reinforcement learning and dealing with dynamic systems.

Who Should Attend

Data Analyst or roles that require solving data related problems.

Prerequisites

“Introduction to Rapidminer”
Minimum Diploma and good English knowledge

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This programme is part of

Professional Certificate in Machine Learning for Business

Types of Available Fundings

SkillsFuture Funding

SkillsFuture Credit

Enhanced Training Support

SFEC

UTAP

PSEA

Instructors

Mr Mario Favaits

Programme Fees

	Singapore Citizens		Singapore PRs	Enhanced Training Support for SMEs	International Participants
	39 years old or younger	40 years old or older	Singapore PRs	Enhanced Training Support for SMEs	International Participants
Full Programme Fee	S$3,150.00	S$3,150.00	S$3,150.00	S$3,150.00	S$3,150.00
Less: SSG Grant Amount	S$2,205.00	S$2,205.00	S$2,205.00	S$2,205.00	-
Nett Programme Fee	S$945.00	S$945.00	S$945.00	S$945.00	S$3,150.00
9% GST on Nett Programme Fee	S$85.05	S$85.05	S$85.05	S$85.05	S$283.50
Total Nett Programme Fee Payable, Including GST	S$1,030.05	S$1,030.05	S$1,030.05	S$1,030.05	S$3,433.50
Less Additional Funding	-	S$630.00	-	S$630.00	-
Total Nett Programme Fee Payable, Including GST, after additional funding from the various funding schemes	S$1,030.05	S$400.05	S$1,030.05	S$400.05	S$3,433.50

Total Nett Programme Fee Payable, including GST, after additional funding from various funding schemes.
Participants must fulfill at least 75% attendance and pass all assessment components to be eligible for SSG funding.
Please note that all external funding for courses is limited in duration and subject to eligibility and availability.

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 unsupervised learning in our article, “Unsupervised Learning: A Deep Dive” or download a PDF copy here.

Mr Mario Favaits

Advanced Computing for Executives