Unit 6 - Machine Learning Algorithms

Correct answer: A. Learning patterns from data and experience

Machine learning enables a computer system to find patterns in data or examples and use them to make predictions or decisions on new inputs. In traditional rule-based programming, instructions are explicitly written for each situation. Merely storing data and assembling hardware do not constitute machine learning.

Correct answer: C. चित्रों को पशु और पक्षी श्रेणियों में बाँटना

Classification is a machine-learning task in which data is assigned to one of two or more predefined labels. Sorting pictures into animal and bird categories is classification because each picture receives a category based on its features. Lifting a chair, drinking water, and hammering a nail are physical activities; they do not involve assigning data to labels. Therefore, option C is the only suitable example.

Correct answer: B. To help the machine recognize patterns

Examples show the system how different inputs may be related to categories, outcomes, or labels. By examining many examples, a learning system can discover useful patterns and apply them to new inputs. Decorating a machine, reducing paper, or emptying a room has no connection with the purpose of training examples.

Correct answer: A. Enabling a machine to learn patterns from examples or data

During training, an AI model processes examples and adjusts its internal parameters so that it can recognise useful patterns or relationships. Later, it can use what it learned to classify or predict new inputs. Assembling hardware, deleting data, or blocking inputs does not teach the model how to perform its task.

Correct answer: A. Learning patterns from data

Learning patterns from training examples allows an AI system to generalise: it can use what it learned from known cases to classify or estimate outcomes for new cases. A fixed rule program does not learn unless it is changed, random answers are not learning, and human approval is a control method rather than the learning characteristic itself.

Correct answer: A. To teach an AI model to recognize patterns

Training data contains examples such as images, text, measurements, or labelled records. The model uses these examples to learn relationships and patterns that can later support classification or prediction on new data. Training data does not physically expand memory, speed up the internet, or change keyboard functions.