Category: Theory
13. Learning: Genetic Algorithms
Mimicking chromosomes Reproduction loop Strings of 0 and 1 simulate chromosomes in binary systems. An initial population of chromosomes is allowed to mutate (modification of certain bit in the strings), cross over with other strings during replication, or to remain the same. Mutation: enables a step by step modification of chromosomes, allowing hill-climbing to improve…
12b: Deep Neural Nets
Image recognition by a deep neural net Convolution: a neuron looks for patterns in a small portion (10×10 px) of an image (256×256 px), the process is repeated by moving this small area little by litte. Pooling: The result of the convolution is computed as a point for each portion analyzed. By a similar step…
12a: Neural Nets
Modeling biological neurons Neural nets are modeled upon real biological neurons, which have the following characteristics: All or none: the input from each entry in the neural net is either O or 1, the output is also 0 or 1 Cumulative influence: the influence of various input neurons accumulates to produce the final output result,…
11. Learning: Identification Trees, Disorder
Identification Trees Problems Non-numeric data Not all characteristics matter Some do matter but not all of the time Cost: certain tests may be more expensive than others Occam’s razor The objective is to build the smallest tree possible (to reduce costs and computation) and because the simplest explanation is always the best. Testing data Small…
10. Introduction to Learning, Nearest Neighbors
Learning Regularity: “Bulldozer computing” Nearest neighbors: pattern recognition Neural nets: mimic biology Boosting: theory Constraints: human-like learning One-shot Explanation-based learning Nearest neighbor A detection mechanism generates a vector of features. These features are converted in a vector of values that is compared to a library of possibilities to find the closest match in order to…