week3_1

Chapter 5 Slides

Opening Example

 Opening Vignette

 Healthcare

Basic Concepts of Neural Networking

 Neural computing

 Artificial Neural Network (ANN)

 Pattern Recognition

 Neurons

 Axons

 Dendrites

 Biological vs. ANN

Neural Network architectures

 Recurrent NNA Kohonen Network (SOM) / Hopfield Network

Support Vector Machines

Nearest Neighbor Method for Prediction

Naïve Bayes Method for Classification

 Naïve Bayes is a simple probability-based classification method (a machine- learning tech-nique that is applied to classification-type prediction problems) derived from the well-known Bayes theorem. The method requires the output variable to have nominal values.

Bayesian Networks

 BN is a powerful tool for representing dependency structure in a graphical, explicit, and intuitive way. It reflects the various states of a multivariate model and their probabilistic relationships.

Ensemble modeling

 Combinations of the outcomes produced by two or more analytics models into a compound output. Ensembles are primarily used for prediction modeling when the scores of two or more models are combined to produce a better prediction.

Wrap Up

 Review the Chapter highlights

 Review the key terms

 Complete the weekly homework