Machine Learning

Machine learning, as the name suggests is the ability of a machine to learn, even without programming it explicitly. It is a type of Artificial Intelligence which is based on algorithms to detect patterns in data and adjust the program actions accordingly.

Example:

Facebook’s News feed personalizes each of its members’ feed using machine learning. The software uses statistical and predictive analytics to identify patterns in the user’s data and uses it to populate the user’s Newsfeed. If a user reads and comments on a particular friend’s posts then the news feed will be designed in a way that more activities of that particular friend will be visible to the user in his feed. The advertisements are also shown in the feed according to the data based on user’s interests, likes, and comments on Facebook pages.

Components of Machine learning algorithms:

1.Representation: It includes the representation of data. It is done through decision trees, neural networks, support vector machines, regressions and others.

2.Evaluation: It is the way to evaluate programs. It involves accuracy, probability, squared error, margin, and others.

3.Optimization: It is the way programs are generated and it uses combinatorial optimization, convex optimization, and constrained optimization.