Machine Learning Overview

Machine learning is a branch of artificial intelligence where computers learn from data, and adapt the computational models to enhance performance. It is a method of analysis that allows computers to reveal information within data. The “learning” is not the type of learning that you and I do. Instead, it is a systematic approach to finding an appropriate data transformation from inputs to output.

Why Machine Learning?

• Computers can sort through data faster than humans can.
• Computers can identify patterns quickly and use these patterns for predictions or classifications.
• Machine learning can handle noisy data – it doesn’t find a perfect answer, but rather a “really good” answer.

Problems that ML can solve

• Regression
  • Regression models determine a mathematical model for the relationship among features or attributes so that an outcome can be predicted.
  • Results can be any value within a possible range (e.g., what will the average Earth temperature be in 2050?)
• Classification
  • Classification techniques identify a combination of attributes that best fits a class or category so that an object can be classified.
  • Results can be from a list of known possibilities (e.g., is the tumor benign or malignant?)

Types of Machine Learning

• Supervised Learning:
  • A data set exists where the samples can be categorized into two or more classifications.
  • The computer uses the data set to learn how to predict the classification of an unknown sample.
  • Examples include Decision Trees and Deep Learning
• Unsupervised Learning:
  • The collected data has no known classification or pattern.
  • The computer must identify the groups or hidden structures within the data.
  • Examples include Dendograms, K-means clustering, Self-organizing Maps
• Reinforcement Learning:
  • Computer learns from positive or negative feedback
  • Example includes Swarm intelligence

Data for Machine Learning

• Data are plural, and the singular is datum
• For many Machine Learning algorithms, the data are expected to be in a table format, where:
  • each row represents an object, and
  • each column has the measurements for a specific attribute or feature of the object
• For supervised learning, the classifications of the objects must be known.
• The data with known classifications are divided into a training set and a testing set.
• The data are used to develop a model.
  • The training data are submitted to an algorithm that will fit a model to the data.
  • The test data are submitted to the model to produce predicted classifications and determine the accuracy of the model.
• Finally, the model can be used to predict classifications for “unknown” data.

Ideas behind Machine Learning

The algorithm determines the best mathematical model for the code. However, you still need to provide a “framework” for the algorithm.The framework provides the algorithm with tools for performing the learning:

• Machine Learning Algorithm
• Data Measurements
• Model defining the relationship between the input and the output
• Label or Classification