1. Business Analytics Several odds and ends Copyright © 2016 Curt Hill

2. Introduction This presentation considers several discrete pieces not yet covered in chapter 6 Expert systems Neural networks Visualization Copyright © 2016 Curt Hill

3. Expert Systems The idea of an expert system has been around for thirty or more years Developed originally in field of Artificial Intelligence in the 1980s They realized that no amount of intelligence could compensate for a lack of facts One of the earliest attempts to augment AI with substantial knowledge Copyright © 2016 Curt Hill

4. Rules The knowledge that was accumulated was in the form of facts and IF THEN statements The facts were either absolute or accumulated about a specific scenario that the expert system was considering The If Then statement is the logical implication statement Copyright © 2016 Curt Hill

5. Example Suppose that you want to go prospecting for minerals Hire a real life expert on the topic Interrogate the expert on the facts –What to look for on the surface Probe deeper on tests to run The goal is also to find the relationships that the expert knows about –These are the If statements Copyright © 2016 Curt Hill

6. Inference Engine The heart of the expert system is an inference engine –AKA automated reasoning system This piece of software takes the facts and If statements and comes to the logical conclusion The inference engine knows nothing about geology or any other expert’s domain Instead it can deal with the facts and ifs to come to its conclusions Copyright © 2016 Curt Hill

7. Expert Systems Thus there are two pieces to these An inference engine –Stronger ones may use more logical statements than just the implication The knowledge base –The facts and logical statements Another name for an expert system is a knowledge based system Copyright © 2016 Curt Hill

8. Neural Networks Another type of processing that was developed in AI This one is based upon our understanding about how the brain works The fundamental notion is that a neuron has dendrites that accept input and an axon that fires under certain conditions Copyright © 2016 Curt Hill

9. Neuron Picture Copyright © 2016 Curt Hill

10. The Software Neuron The neuron is simulated by a software unit that takes several numeric inputs –This unit may be called a neuron, neurode, processing element and other things It applies a weighting value to each one If the sum of these weighted values exceeds a threshold then output is produced Copyright © 2016 Curt Hill

11. Layers One neuron does not constitute intelligence The neurons are arranged in layers –An initial layer, that deals with inputs –Hidden layers that process the values produced Each neuron may share inputs with others near it See the following picture Copyright © 2016 Curt Hill

12. Neural Layers Copyright © 2016 Curt Hill

13. Training Each input to the neuron is weighted The setting of these weights is usually called the training process The weights are set such that the neural network produce the desired result This is often the recognition of an image or other computationally complicated tasks Copyright © 2016 Curt Hill

14. Machine Learning One of the advantages of a neural network is that it may learn The training process is how it learns recognition Thus it is one example of machine learning –There are others as well –One of these is clustering Copyright © 2016 Curt Hill

15. Clustering Suppose you have some items and several measurements for each of them The goal is to classify these into subsets Each member of a subset should be more closely related to the others than to other subsets Copyright © 2016 Curt Hill

16. Cluster Picture Copyright © 2016 Curt Hill

17. Clustering Again The previous picture was two dimensional –Each point had two measurements In practice it could be many more We are still able to compute a Euclidean distance between any two points using Pythagorean theorem The harder part is to assign the points to a subset –Many algorithms for that Copyright © 2016 Curt Hill

18. Visualization They say that one picture is worth a thousand words –This may be understated People are much better at processing visual material than text Therefore rendering information in terms of graphs and charts conveys more information –More quickly as well This is particularly true with the data sizes in question Copyright © 2016 Curt Hill

19. Examples Dashboard – a way to monitor a system or the environment –A visual summary of Key Performance Indicators Visual Analytics –Interactive graphics for complex analysis Geographic Information Systems –Visualizing geographic or demographic information Copyright © 2016 Curt Hill

20. Dashboard for Restaurant

21. GIS Dashboard Copyright © 2016 Curt Hill

22. Finally A particular enterprise may find any or all of these useful As computing and storage technologies come down in price we will see a lot more of these Any advantage a business can obtain may be the deciding factor Copyright © 2016 Curt Hill