JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY HYDERABAD B.Tech III Year MACHINE LEARNING Previous Question Paper (February - 2022 - R18)

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. A problem is considered well-posed when these three components—the task, the performance measure, and the source of experience—are clearly and explicitly defined.

To well-define a learning problem, one must specify the following three key features:

• Task (T): This defines the specific activity the system is expected to perform. Examples include classifying emails as spam or predicting the price of a house. The task should be clearly delineated to identify what the system is learning to do.
• Performance Measure (P): This is a quantitative metric used to evaluate how well the system performs the task. For classification, this might be accuracy or error rate; for regression, it might be mean squared error. This measure provides the objective function that the learning algorithm aims to optimize.
• Experience (E): This represents the data or information provided to the system. It can be categorized as supervised (labeled data), unsupervised (unlabeled data), or reinforcement learning (interaction with an environment). The quality and quantity of this experience directly dictate the potential for the system to improve its performance.

By explicitly defining these three elements, a developer ensures that the learning process is measurable, the objective is clear, and the data source is appropriate for the intended goal.