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

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

Analytics, Data Science and A I: Systems for Decision Support Eleventh Edition

Chapter 6

Deep Learning and Cognitive

Computing

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Introduction to Deep Learning

• The placement of Deep Learning within the overarching

A I-based learning methods

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Introduction to Deep Learning

• Differences between Classic Machine-Learning Methods

and Representation Learning/Deep Learning

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Process of Developing Neural-Network Based

Systems

• A process with constant

feedbacks for changes and

improvements!

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Backpropagation for A N N Training

1. Initialize weights with random values

2. Read in the input vector and the desired output

3. Compute the actual output via the calculations

4. Compute the error.

5. Change the weights by working backward

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Deep Neural Networks

• Deep: more hidden layers and number of neurons

• Uses Graphics Processing Units (G P U)

– With programming languages like C U D A by N V I D I A

• Process larger datasets

• There are different types and capabilities of Deep Neural

Networks for different tasks/purposes

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Convolutional “Deep” Neural

Networks

• Most popular M L P-base D L method

• Used for image/video processing, text recognition

• Has at least one convolution weight function

– Convolutional layer

• Convolutional layer involves Polling (sub-sampling)

– Consolidating large vectors into a smaller size

– Reducing the number of model parameters

– Keeping only the important features

– There can be different types of polling layers

Analytics, Data Science and A I: Systems for Decision Support Eleventh Edition

Chapter 6

Deep Learning and Cognitive

Computing

Copyright © 2020, 2015, 2011 Pearson Education, Inc. All Rights Reserved

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Recurrent Neural Networks (R N N) &

Long Short-Term Memory (L S T M)

• L S T M is a variant of R N N

– In a dynamic network, the weights are called the long-

term memory while the feedbacks role is the short-

term memory

Typical Long

Short-Term

Memory (L S T M)

Network

Architecture

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Conceptual Framework for Cognitive

Computing and Its Promises

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Cognitive Search

• Can handle a variety of data types

• Can contextualize the search space

• Employ advanced A I technologies.

• Enable developers to build enterprise-specific search

applications

Analytics, Data Science and A I: Systems for Decision Support Eleventh Edition

Chapter 5

Machine-Learning Techniques for

Predictive Analytics

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Processing Information in Artificial

Neural Networks

• A single neuron (processing element – P E) with inputs

and outputs

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Elements of A N N

• Processing element (P E)

• Network information processing

– Inputs

– Outputs

– Hidden layers

– Connection weights

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Neural Network Architectures

• Architecture of a neural network is driven by the task it is

intended to address

– Classification, regression, clustering, general

optimization, association

• Feedforward, multi-layered perceptron with

backpropagation learning algorithm

– Most popular architecture:

– This A N N architecture will be covered in Chapter 6

• Other A N N Architectures – Recurrent, self-organizing

feature maps, hopfield networks, …

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Support Vector Machines (S V M)

• S V M are among the most popular machine-learning

techniques.

• S V M belong to the family of generalized linear models…

(capable of representing non-linear relationships in a

linear fashion)

• S V M achieve a classification or regression decision based

on the value of the linear combination of input features.

• Because of their architectural similarities, S V M are also

closely associated with A N N.

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Support Vector Machines (S V M)

• Many linear classifiers (hyperplanes) may separate the

data

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Support Vector Machines (S V M)

Rohrer, B (2017) How SVM work

https://www.youtube.com/watch?v=-Z4aojJ-pdg (9:00 & 10:00)https://www.youtube.com/watch?v=-Z4aojJ-pdg

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The Process of Building a S V M

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k-Nearest Neighbor Method (k-N N)

• A N Ns and S V M s → time-demanding, computationally

intensive iterative derivations

• k-N N a simplistic and logical prediction method, that

produces very competitive results

• k-N N is a prediction method for classification as well as

regression types (similar to A N N & S V M)

• k-N N is a type of instance-based learning (or lazy

learning) – most of the work takes place at the time of

prediction (not at modeling)

• k : the number of neighbors used in the model

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k-Nearest Neighbor Method (k-N N)

• The answer to

“which class a

data point

belongs to?”

depends on the

value of k

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Naïve Bayes Method for

Classification

• Naïve Bayes is a simple probability-based classification

method

– Naïve – assumption of independence among the input

variables

• Output variable must be nominal

– Can use both numeric and nominal input variables

• Can be used for both regression and classification

• Naïve based models can be developed very efficiently

and effectively

– Using maximum likelihood method

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Naïve Bayes Method for

Classification

• Process of Developing a Naïve Bayes Classifier

• Training Phase

1. Obtain and pre-process the data

2. Discretize the numeric variables

3. Calculate the prior probabilities of all class labels

4. Calculate the likelihood for all predictor

variables/values

• Testing Phase

– Using the outputs of Steps 3 and 4 above, classify the

new samples

▪ See the numerical example in the book…

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Ensemble Modeling

• Ensemble – combination of models (or model outcomes)

for better results

• Why do we need to use ensembles:

– Better accuracy

– More stable/robust/consistent/reliable outcomes

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Types of Ensemble Modeling

Figure 5.20 Simple

Taxonomy for Model

Ensembles.

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Types of Ensemble Modeling

Figure 5.20 Bagging-Type Decision Tree Ensembles.

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Types of Ensemble Modeling

Figure 5.20 Boosting-Type Decision Tree Ensembles.

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Ensemble Modeling

• Variants of Bagging & Boosting (Decision Trees)

– Decision Trees Ensembles

– Random Forest

– Stochastic Gradient Boosting

• Stacking

– Stack generation or super learners

• Information Fusion

– Any number of any models

– Simple/weighted combining

( )

Homogeneous

model types

decision trees

    

( )

Homogeneous

model types

decision trees

    

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Ensembles – Pros and Cons

Table 5.9 Brief List of Pros and Cons of Model Ensembles Compared to

Individual Models.

PROS (Advantages) Description

• Accuracy Model ensembles usually result in more accurate models than individual models.

• Robustness Model ensembles tend to be more robust against outliers and noise in the data set

than individual models.

• Reliability (stable) Because of the variance reduction, model ensembles tend to produce more stable,

reliable, and believable results than individual models.

• Coverage Model ensembles tend to have a better coverage of the hidden complex patterns in

the data set than individual models.

CONS (Shortcomings) Description

• Complexity Model ensembles are much more complex than individual models.

• Computationally

expensive

Compared to individual models, ensembles require more time and computational

power to build.

• Lack of transparency

(explainability)

Because of their complexity, it is more difficult to understand the inner structure of

model ensembles (how they do what they do) than individual models.

• Harder to deploy Model ensembles are much more difficult to deploy in an analytics-based Managerial

decision-support system than single models.