Machine Learning Intro

Machine Learning

Machine learning is the branch of computer science that utilizes past experience to learn from and use its knowledge to make future decisions. Machine learning is at the intersection of computer science, engineering, and statistics. The goal of machine learning is to generalize a detectable pattern or to create an unknown rule from given examples. An overview of machine learning landscape is as follows:

Machine learning is broadly classified into three categories but nonetheless, based on the situation, these categories can be combined to achieve the desired results for particular applications:

• Supervised learning: This is teaching machines to learn the relationship between other variables and a target variable, similar to the way in which a teacher provides feedback to students on their performance. The major segments within supervised learning are as follows:
  • Classification problem
  • Regression problem
• Unsupervised learning: In unsupervised learning, algorithms learn by themselves without any supervision or without any target variable provided. It is a question of finding hidden patterns and relations in the given data. The categories in unsupervised learning are as follows:
  • Dimensionality reduction
  • Clustering
• Reinforcement learning: This allows the machine or agent to learn its behavior based on feedback from the environment. In reinforcement learning, the agent takes a series of decisive actions without supervision and, in the end, a reward will be given, either +1 or -1. Based on the final payoff/reward, the agent reevaluates its paths. Reinforcement learning problems are closer to the artificial intelligence methodology rather than frequently used machine learning algorithms.

In some cases, we initially perform unsupervised learning to reduce the dimensions followed by supervised learning when the number of variables is very high. Similarly, in some artificial intelligence applications, supervised learning combined with reinforcement learning could be utilized for solving a problem; an example is self-driving cars in which, initially, images are converted to some numeric format using supervised learning and combined with driving actions (left, forward, right, and backward).