Unit-5 Applications of Computer Science
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Unit-5 Applications of Computer Science
Answer: AI stands for Artificial Intelligence, the field of computer science that creates systems able to perform tasks that normally require human intelligence.
Answer: Natural Language Processing deals with understanding, interpreting, and generating human language.
Answer: AI aims to build machines that think, reason, learn, and solve problems in ways that resemble human intelligence.
Answer: Python is a programming language used to build AI systems, not a branch of Artificial Intelligence itself.
Answer: Robotics is the field where AI techniques are combined with mechanical engineering to build intelligent machines.
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Answer: Artificial Intelligence refers to machines performing tasks such as reasoning, learning, and problem solving that typically need human intelligence.
Answer: Machine Learning is a subfield of AI in which systems learn patterns from data and improve their performance with experience.
Answer: The main goal of AI is to create systems that can simulate human-like thinking, reasoning, and decision-making.
Answer: A chess-playing program that plans moves and adapts to the opponent is a classic example of AI at work.
Answer: Deep learning is a specialized subset of machine learning that uses multi-layered neural networks to learn from large amounts of data.
Answer: Computer Vision enables machines to identify and interpret visual information such as images and videos.
Answer: Machine learning algorithms analyze large datasets and discover patterns that help the system make predictions or decisions.
Answer: Machine learning systems improve by analyzing data, finding patterns in it, and applying what they have learned to new situations.
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Answer: In supervised learning, the model is trained on labeled examples where the correct output is already known.
Answer: Unsupervised learning finds hidden patterns and groups similar data items together without using labeled examples.
Answer: During training, a model is fed data and adjusts its internal parameters to reduce errors and improve accuracy.
Answer: Spam filters learn from many examples of spam and legitimate mail to automatically classify new emails.
Answer: Machine learning algorithms improve their performance by learning from data and past experience rather than through fixed rules.
Answer: Recommendation systems use machine learning to analyze your history and suggest content you are likely to enjoy.
Answer: Supervised learning trains a model using input-output pairs so it can predict the correct output for new inputs.