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Supervised Learning

  • Most machine learning is supervised learning.
  • Supervised learning algorithms are “trained” using labeled examples where the desired output is known.

Unsupervised Learning

  • About 10 to 20 percent of machine learning is unsupervised learning, although this area is growing rapidly.
  • Unsupervised learning is a type of machine learning where the system operates on unlabeled examples. In this case, the system is not told the “right answer.”
  • The algorithm tries to find a hidden structure or manifold in unlabeled data.

Semisupervised Learning

  • Semi-supervised learning is a class of supervised learning tasks and techniques that also make use of unlabeled data for training – typically a small amount of labeled data with a large amount of unlabeled data.
  • Semi-supervised learning falls between unsupervised learning (without any labeled training data) and supervised learning (with completely labeled training data).

Reinforcement Learning

  • Reinforcement learning has three primary components: the agent (i.e., the learner or decision maker, the environment (i.e., everything the agent interacts with), and actions (i.e., what the agent can do).

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MANUFACTURING

  • Determining propensity to buy
  • Estimating warranty reserves
  • Forecasting demand
  • Optimizing process and predicting maintenance
  • Orchestrating telematics

RETAIL

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  • Providing predictive inventory planning
  • Driving recommendation engines, upsell, and cross-sell opportunities
  • Automating intelligent market segmentation and targeting 

HEALTHCARE AND LIFE SCIENCES

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  • Providing real-time alerts and patient diagnostics
  • Identifying diseases and risk stratification
  • Optimizing patient triage
  • Driving proactive health management
  • Analyzing healthcare

ENERGY, UTILITIES, & FEEDSTOCK

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  • Analyzing power usage
  • Processing seismic data
  • Optimizing energy demand and supply
  • Automating intelligent grid management
  • Recommending customer pricing

FINANCIAL SERVICES

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  • Providing risk analysis and regulation
  • Evaluating credit
  • Segmenting customers
  • Recommending cross-sell & upsell opportunities
  • Automating sales & marketing campaigns

TRAVEL & HOSPITALITY

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  • Analyzing traffic patterns and congestion management
  • Scheduling aircraft
  • Creating dynamic prices
  • Automating social feedback & interaction

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