Machine Learning is the study of how to build computer systems that adapt and improve with experience. It is a subfield of Artificial Intelligence and intersects with cognitive science, information theory, and probability theory, among others.
Classical AI deals mainly with deductive reasoning, learning represents inductive reasoning. Deductive reasoning arrives at answers to queries relating to a particular situation starting from a set of general axioms, whereas inductive reasoning arrives at general axioms from a set of particular instances.
Classical AI often suffers from the knowledge acquisition problem in real life applications where obtaining and updating the knowledge base is costly and prone to errors. Machine learning serves to solve the knowledge acquisition bottleneck by obtaining the result from data by induction.
Machine learning is particularly attractive in several real life problem because of the following reasons:
• Some tasks cannot be defined well except by example
• Working environment of machines may not be known at design time
• Explicit knowledge encoding may be difficult and not available
• Environments change over time
• Biological systems learn Recently, learning is widely used in a number of application areas including,
• Data mining and knowledge discovery
• Speech/image/video (pattern) recognition
• Adaptive control
• Autonomous vehicles/robots
• Decision support systems
Formally, a computer program is said to learn from experience E with respect to some class of tasks T
and performance measure P , if its performance at tasks in T , as measured by P, improves with experience
E Thus a learning system is characterized by:
• task T
• experience E, and
• performance measure P