Machine Learning

Supervised Learning

It is the term used whenever a network is “trained” on a predefined data set. Based on pre-defined data training, the network can make accurate decisions when it receives new data. Example: You can use a Machine Learning trial results dataset for training that has convictions, settlements and acquittals to train a highly reliable case analysis classifier.

Unsupervised Learning

Term used when a network can automatically find patterns and relationships in a dataset. Example: Analysis of a customer dataset and automatic clustering of these customers related to a feature set, without the program having any prior knowledge of the data.

Classification

Classification is a sub-category of supervised learning. Sorting is the process of taking some kind of input and labeling it. Classification systems are generally used when forecasts are of a distinct nature, ie a simple yes or no. Example: Mapping an image of a person and classification as male or female.

Regression

Another supervised learning subcategory used when the value being predicted differs from a “yes or no” and follows a continuous spectrum. Regression systems could be used, for example, to answer the questions, “How much does it cost?” Or “How many are there?”

Decision Trees

A decision tree is a decision support tool that uses a decision tree graph or model and its possible consequences. A decision tree is also a way to visually represent an algorithm.

Generative Model

In probability and statistics, a Generator Model is a model used to generate data values when some parameters are unknown. Generator models are used in Machine Learning for either data modeling directly or as an intermediate step in forming a conditional probability density function.

Discriminative Model

Discriminative Model, or conditional models, are a class of models used in Machine Learning to model the dependency of a y variable on an x variable. As these models try to calculate conditional probabilities, that is, p (y | x) are often used in supervised learning. Examples include logistic regression, SVMs, and neural networks.

Deep Learning

This has been a topic much discussed recently. Basically, deep learning refers to a category of Machine Learning algorithms that often use artificial neural networks to generate models. Deep learning techniques, for example, have been very successful in solving image recognition problems because of their ability to choose the best features as well as expressing layers of representation. Inspired by biological neural networks, artificial neural networks are a network of interconnected nodes that make up a model. They can be defined as statistical learning models that are used to calculate or approximate functions that depend on a large number of inputs. Neural networks are usually used when the input volume is too large for the conventional machine learning approaches previously discussed.