Business Applications Relying on Unsupervised & Reinforcement Learning
Dates: 12 Jun, 13 Jun, 14 Jun 2023 | 9am – 5.30pm | Classroom Learning
Duration: 3 Days
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.
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.
Minimum Diploma and good English knowledge.
(Click their photos to view their short biographies)
Mr Mario Favaits
Mr Mario Favaits
Over the past 25 years, Mario held several sales and operations (P&L) leadership positions at various multinationals across different industries including Enterprise, Automotive, Public Transport, and Software. Currently, he serves as the Executive Director of Services Sales APJ at Crayon, a fast-growing, listed, Oslo-based IT powerhouse. Crayon helps customers to innovate with scalable AI.
Prior to joining Crayon, Mario held leadership positions at Alstom, Continental, Siemens, Oracle, and SMRT, Singapore’s largest Public Transport Operator. Between 2014 and 2019, he was a member of SMRT’s executive leadership team. In 2019, Mario joined the AI and IoT startup scene as an advisor and/or investor.
Mario is the co-chair of EuroCham’s Digital Economy Committee and is the author of an online AI course for Executives, in collaboration with Deloitte.
He holds a Master of Science in Mechanical and Electrical Engineering from the University of Brussels, an MBA from the University of Antwerp Management School, and a Master of Laws from the University of Liverpool.
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.
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TGS-2022015668 (Classroom Learning) / TGS-2022015677 (Synchronous e-learning)
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Course Fee Breakdown
Singapore Citizens39 years old or younger
Singapore Citizen40 years old or older