1. Prepared by: Mahmoud Rafeek Al-Farra
College of Science & Technology
Dep. Of Computer Science & IT
BCs of Information Technology
Data Mining
Chapter 4_4: Classification Methods
(Examples)
Prepared by: Mahmoud Rafeek Al-Farra
2013

2. Course’s Out Lines Introduction Data Preparation and Preprocessing
Data Representation
Classification Methods
Evaluation
Clustering Methods
Mid Exam
Association Rules
Knowledge Representation
Special Case study : Document clustering
Discussion of Case studies by students

3. Out Lines
Naïve Bayesian Classifiers
Artificial Neural Networks

4. Naïve Bayesian Classifiers
A Bayes classifier is a simple probabilistic classifier based on applying Bayes' theorem (from Bayesian statistics) with strong (naive) independence assumptions.
A more descriptive term for the underlying probability model would be "independent feature model".

5. What is ANN?
An artificial neural network can be defined as a model of reasoning based on the human brain.

6. What is ANN?
Analogy between biological and artificial neural networks

7. Artificial Neural Networks
ANNs can be defined as a model of reasoning based on the human brain.
A NN is a system of processing units, connections and weights associated with the connections which propagates activation from its input units to its output units, augmented by a learning rule.

8. Artificial Neural Networks
ANN can be viewed as weighted directed graphs in which artificial neurons are nodes and directed edges labeled with weights are connections between neuron outputs and neuron inputs.

9. Simple computing element in ANN

10. Artificial Neural Networks
The behavior of the network is determined by the combination of its architecture and its set of weights which is known as the learning of network.

11. Classification of ANN architectures

12. Feedforward neural network

13. Feedback neural network

14. Next …
Evaluation

15. Thanks