Complete the following assignment in one MS word document:
Write post 1 (Chapter 5): What is the relationship between Naïve Bayes and Bayesian networks? What is the process of developing a Bayesian networks model?
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There must be at least one APA formatted reference (and APA in-text citation) to support the thoughts in the post. Do not use direct quotes, rather rephrase the author’s words and continue to use in-text citations.
Write post 2 (Chapter 6): List and briefly describe the nine-step process in conducting a neural network project.
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Analytics, Data Science, & Artificial Intelligence,
11th Edition
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
Analytics, Data Science, & Artificial Intelligence,
11th Edition
Chapter 6 Slides
Opening Example
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.
Introduction to Deep Learning
Deep learning with AI-based learning
Process of developing neural network-based systems
Review Figure 6.11
Learning process in ANN
Supervised learning
Performance function
Over-fitting
Illuminating the black box of Ann
Deep Neural Networks
Convolution Neural Networks
Pooling
Convolution Network unit
Recurrent networks and long short-term memory networks
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.
Computer frameworks for implementation of deep learning
Torch
Caffe
TensorFlow
Theano
Figure 6.36
Cognitive computing
Wrap Up
Review the Chapter highlights
Review the key terms
Complete the weekly homework