1. Eco 6380 Predictive Analytics For Economists Spring 2016
Professor Tom Fomby
Department of Economics
SMU

2. Presentation 7 Artificial Neural Networks: Prediction and Classification Chapter 11 in SPB

3. OUTLINE I. Historical Motivation
A. Resemblance to human learning and memory in the brain
B. Ability to Generalize from particulars
C. Main Strengths: High predictive performance and ability to capture very complex relationships between predictors and a response

4. OUTLINE II. Concepts and Structures of ANNs
A. Input Layer (Input Nodes therein)
B. Hidden Layer(s) (Input Nodes therein)
C. Output Layer (Output Node(s))
D. Multilayer Feed-Forward ANNs
E. Nerves (links) and Neurons (nodes)
F. Possible Transfer (Squashing or Activation) Functions: Linear, Logistic, Exponential, Arc Tangent, etc.

5. OUTLINE III. Some Diagrams of N-M-R-Q ANNs:
N = number of input nodes in Input Layer
M = number of hidden nodes in first Hidden Layer
R = number of hidden nodes in second Hidden Layer (if necessary)
Q = number of output nodes: Q = 1 with conventional prediction problem; Q = 2 for binary classification or = q for q unordered (nominal) categories. If the classification is an ordered classification problem then Q = 1 with the output variable being normalized to the [0,1] interval with increments of 1/(q-1) representing the ranks of the ordinal variable starting with 0 and ending with 1.

6. Now for a Discussion of the Various Parts of this Outline go to the pdf file ANNs.pdf

7. Classroom Exercise: Exercise 5