End

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• What is a Neural Network?
• Collection of neurons
• Computes some function
• Takes input
• Produces output
• Can learn

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• Human Brain Function
• Human brain can generalize from abstract
• Recognize patterns in the presence of noise
• Recall memories
• Make decisions for current problems based on
  prior experience

• Neural Network Neurons
• Receives n-inputs
• Multiplies each input by its weight
• Applies activation function to the sum of results
• Outputs result

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• What is an Artificial Neural Network (ANN)?
• The computational ability of a digital computer
  combined with the desirable functions of the
  human brain.

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• Biological Neurons

• Artificial Neurons

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• How does a neural network learn?
• A neural network learns by determining the
  relation between the inputs and outputs.
• By calculating the relative importance of the
  inputs and outputs the system can determine such
  relationships.

• Through trial and error the system compares its
  results with the expert provided results in the
  data until it has reached an accuracy level
  defined by the user.
• With each trial the weight assigned to the inputs
  is changed until the desired results are reached.

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• How the Process Works ?
• Step 1 Initialisation
• Set initial weights to random numbers in your
  range
• Step 2 Activation
• Activate the perceptron by applying inputs and
  desired output.( training set data) Calculate
  the actual output at iteration .

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• Step 3 Weight training
• Update the weights of the perceptron.The weight
  correction is computed by the delta rule
• Step 4 Iteration
• Increase iteration p by one, go back to Step 2
  and repeat the process until convergence.

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• Different Learning Algorithms
• Backpropagation
• Delta Learning Rule
• Forward Propagation
• Hebb Learning Rule
• Simulated Annealing
• Genetic Algorithms

• Types of Networks
• Multi-Layer-Perceptron
• Hopfield Net
• Kohonen Feature Map
• Adaptive Resonance Theory (Art), Fussy ArtMap
• Type of Learning
• Supervised
• Unsupervised

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Muli-Layer Perceptron
Hopfield Net
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• Neural Networks can be extremely complex and hard
  to use
• The programs are filled with settings you must
  input and a small amount of data will cause your
  predictions to have error
• The results can be very hard to interpret as well
• Dead-end situations are hard to avoid

• Neural networks can find relations that no one
  ever guess they exist
• Since they are data dependent performance will
  improve as sample size increases
• Regression performs better when theory or
  experience indicates an underlying relationship

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• Marketing
• Trading and financial forecast
• Future price estimation
• Exchange rate forcast
• Bankruptcy prediction
• Stock performance and selection
• Portfolio assignment and optimization

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• Large amount of data available in databases
• Customers data available in firms own database
  or can be supplied by companies which sell these
  information
• These information can be applied for marketing
  purposes e.g. direct marketing

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• Direct marketing drives high cost
• Targeting customers who are more likely to spend
  money !
• Direct mailing to customers

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• In this example one charity organization apply
  direct mailing promotion to raise their funds
• Neural network applied to target selection in
  this case
• Neural network should determine those customers
  in data base who would be interested in the offer
  being maid
• Neural network in a learning system which can
  adapt the nonlinearity in the data to capture
  complex

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• There can be different types of databaises with
  variety of data
• There should be most important aspects of a
  successful mailing compa
• Analytical methods (data mining, sensitivity
  analysis, )
• Experiment ( some researches , literature , )
• Causal relations
• experties

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• Data mining offers following representations as
  purchase history
• Recency of purchase
• Frequency of purchase
• Monetary value
• These variables are called RFM variables

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• In working with models like ANN enough care must
  be taken about the process the data
• data preparation publish inpublish out
• Determining causal relations
• Knowledge about customers attitude history

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• Mailing strategy
• Who should be mailed and how frequent
• How frequently Should be organized
• How their promotional material should be
  organized

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• Classification or prediction
• Asymmetrical misclassification costs
• Weigh misclassified responders
• Or target scoring to show customers willingness

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• What is ANN job
• Trained to determine correct set of network
  parameters
• Good indication of the willingness according to
  network inputs
• Indeed, indication of responsive behavior
  regarding their characteristics
• A nonlinear regression model

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• Network configuration
• feed-forward neural networks for practical
  purpases
• number of hidden layers
• methods growing and pruning, heuristic search,
  optimization by evllutionary computation (e.g.
  GA). experiment,...

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• selecting network parameters
• experiments show that one hidden layer provides
  model with sufficient accuracy in target
  selecting
• transfer function hyperbolic tangent sigmoid or
  logistic sigmoid,...

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• data preparetoin
• discription of raw data
• size of data set
• feature selection
• process selected features
• selecting suitabale training and validation sets

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• description of raw data
• a well-known Dutch charuty organization
• more than 725000 supporters in the internal
  database
• database including
• mailing dates,
• amount of donation,
• date of donation in response to a particular
  mailing,...

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• size of data set
• aoge amount of records makes network too complex
  and slow
• data size should be large enough and not too big
• 1000 random records representativies of the whole
  data

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• Feature selection
• neccessarily not all features are useful and
  meaningful for target selection
• features that sumarize the most important aspects
  
• RFM variables
• table 2 the features used for the charity case
  study

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• Neural networks provide ability to provide more
  human-like AI
• Takes rough approximation and hard-coded
  reactions out of AI design (i.e. Rules and FSMs)
• Still require a lot of fine-tuning during
  development

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?????

• Artificial Neural Network in Finance and
  Manufacturing,Joarder Kamruzzaman
• Neural Networks And Their Statistical
  Application, Clint Hagen
• Statistics Senior Seminar 2006
• Neural Networks , Megan Vasta
• Artificial Intelligence Neural Networks, Amir
  Hesami
• Interview with Jeff Hannan, creator of AI for
  Colin McRae Rally 2.0
• Interview with Derek Smart, creator of AI for
  Battlecruiser 3000AD
• Neural Netware, a tutorial on neural networks
• Sweetser, Penny. Strategic Decision-Making with
  Neural Networks and Influence Maps, AI Game
  Programming Wisdom 2, Section 7.7 (439 46)
• Russell, Stuart and Norvig, Peter. Artificial
  Intelligence A Modern Approach, Section 20.5
  (736 48)

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