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TDWI Chicago Update

At TDWI, we have been working hard to navigate this ever-changing landscape in the face of COVID-19, and we want to assure you that the health and well-being of our employees, customers, and vendor partners is our top priority. Therefore, due to the growing concern around the coronavirus (COVID-19), and in alignment with the guidelines laid out by the CDC and WHO, we have decided to merge this year’s TDWI Chicago Conference (May 10-15) with TDWI Orlando 2020 (November 8-13), where it can be a successful experience for everyone. The Chicago 2020 agenda will be replicated at TDWI Orlando 2020.

Our registration team will be in contact with individual registrants and sponsors directly.

Course Description

S6 Unsupervised Machine Learning: Preparing Data & Deploying Analytic Models for Clustering & Association

May 10, 2020

9:00 am - 5:00 pm

Duration: Full Day Course

Level: Intermediate to Advanced

Prerequisite: None

Keith McCormick

Senior Consultant and Trainer

The Modeling Agency

Techniques like cluster analysis, association rules, and anomaly detection are typically called unsupervised learning because they do not require historical outcome data. Although these methods open powerful analytic opportunities, they do not come with a clear path to deployment. They are exploratory in nature and don't generate a propensity score in the same way that supervised learning methods do. So how do you take these association models and automate them in support of organizational decision making? This course will show you how.

This course will demonstrate a variety of examples starting with the exploration and interpretation of candidate models and their applications. Options for acting on results will be explored. You will also observe how a mixture of models including business rules, supervised models, and unsupervised models are used together in real-world situations for various problems like insurance and fraud detection.

You Will Learn

  • When to apply supervised versus unsupervised modeling methods
  • Effective techniques for deploying the results of unsupervised learning
  • Interpret and monitor your unsupervised models for continual improvement
  • Options for inserting unsupervised models into the decision-making process of the organization
  • How to creatively combine supervised and unsupervised models for greater performance

Geared To

  • Analytics practitioners
  • Data scientists
  • IT professionals
  • Technology planners
  • Consultants
  • Business analysts
  • Analytics project leaders