An algorithm is no more than a mathematical equation that can be as simple as a=b+c2or much more complex involving calculus and/or statistical analysis. The approach in Machine Learning is to have algorithms sift through very large volumes of data and find hidden relationships and emerging patterns that suggest trends. For example, most large retail chains apply Machine Learning algorithms to their past and current sales data to discover sales patterns based on store layout alternatives. The corpus of data is typically divided into two parts. One part is used to find patterns and the other part is used to validate that the apparent pattern actually exists and is not just a coincident. There are essentially five kinds of algorithms in Machine Learning: connectionist algorithms in neural networks; genetic algorithms that are based on Darwin’s theory of natural selection; Bayesian algorithms that start with the probability that a certain hypothesis is correct; analogy algorithms that look for an analogous example; and, symbolic algorithms that represent objects as symbols that can be manipulated logically.