Concept & Applicability_

Concept:
Teaching the machine to learn

Machine learning is a subset of Artificial Intelligence that uses statistical and mathematical techniques to give computers the ability to “learn” (ie progressively improve performance of a specific task) with large volumes of data without being explicitly programmed for such activity.

Applicability:
Some practical examples of Machine Learning application

  • Fraud Detection
  • Credit analysis
  • Prediction of repetitive legal processes
  • Anomaly detection in manufacturing processes
  • Behaviour identification and action prediction
  • Prediction of equipment and process failures
  • Text-based sentiment analysis
  • Segmentation and Clustering

Gartner’s latest CIO survey

… among 3,160 CIOs from 98 countries, found that 21% of CIOs are already piloting AI initiatives or have short-term plans for them. Another 25% have medium – or long – term plans.

Gartner Symposium/ITxpo 2017

Main Methods

Some of the methods we apply in 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 labelling 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 modelling directly or as an intermediate step in forming a conditional probability density function.

Discriminative Model

Discriminative Model, or conditional models, is 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 uses 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.

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