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Complete the following assignment in one MS word document:

Chapter 5 –discussion question #1-4 & exercise 6 & internet exercise #7 (go to  neuroshell.com click on the examples and look at the current examples.  The Gee Whiz example is no longer on the page.)

Chapter 6– discussion question #1-5 & exercise 4

When submitting work, be sure to include an APA cover page and include at least two APA formatted references (and APA in-text citations) to support the work this week.

All work must be original (not copied from any source).

Discussion 1

Create a discussion thread (with your name) and answer the following question:

Discussion 1 (Chapter 5): What is the relationship between Naïve Bayes and Bayesian networks? What is the process of developing a Bayesian networks model?

Note: The first post should be made by Wednesday 11:59 p.m., EST. I am looking for active engagement in the discussion.  Please engage early and often.

Your response should be 250-300 words.  Respond to two postings provided by your classmates.

Discussion 2

Create a discussion thread (with your name) and answer the following question:

Discussion 2 (Chapter 6): List and briefly describe the nine-step process in con-ducting a neural network project.

Note: The first post should be made by Wednesday 11:59 p.m., EST. I am looking for active engagement in the discussion.  Please engage early and often.

Your response should be 250-300 words.  Respond to two postings provided by your classmates.

Chapter 5 Slides

▪ Opening Vignette

▪ Healthcare

▪ Neural computing

▪ Artificial Neural Network (ANN)

▪ Pattern Recognition

▪ Neurons

▪ Axons

▪ Dendrites

▪ Biological vs. ANN

▪ Recurrent NNA Kohonen Network (SOM) / Hopfield Network

▪ 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.

▪ 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.

▪ 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.

▪ Review the Chapter highlights

▪ Review the key terms

▪ Complete the weekly homework

Chapter 6 Slides

▪ Opening Vignette

▪ Danske Bank

▪ Results

▪ Realize a 60 percent reduction in false positives with an expectation to reach as high as 80 percent.

▪ Increase true positives by 50 percent.

▪ Focus resources on actual cases of fraud.

▪ Deep learning with AI-based learning

▪ Review Figure 6.11

▪ Learning process in ANN

▪ Supervised learning

▪ Performance function

▪ Over-fitting

▪ Pooling

▪ Convolution Network unit

▪ RNN- specifically designed to process sequential inputs. An RNN basically models a dynamic system where (at least in one of its hidden neurons) the state of the system (i.e., output of a hidden neuron) at each time point t depends on both the inputs to the system at that time and its state at the previous time point t – 1.

▪ Torch

▪ Caffe

▪ TensorFlow

▪ Theano

▪ Figure 6.36

▪ Review the Chapter highlights

▪ Review the key terms

▪ Complete the weekly homework