Short Notes: AI, ML & Data Science

Q: What is Artificial Intelligence (AI)?
A: Engineering of machines/programs that mimic human intelligence and perform actions.
Q: What is Machine Learning (ML)?
A: Subset of AI that enables systems to learn from data and improve without explicit programming.
Q: What is Data Science?
A: Umbrella field covering data gathering, transformation, analytics, ML, AI, visualization, and pattern recognition.
Q: What is Deep Learning?
A: Subfield of ML using neural networks inspired by the human brain, effective for unstructured data.

Q: Difference between traditional programming and ML?
A: Traditional → hard‑coded rules. ML → train models on data to learn rules automatically.

Q: What is Classification?
A: Predicting discrete responses (e.g., spam detection).
Q: What is Clustering?
A: Grouping objects based on similarity/dissimilarity.
Q: What is Trend Analysis?
A: Studying time‑series data to project future events.
Q: What is Anomaly Detection?
A: Identifying unusual patterns (e.g., fraud detection).
Q: What is Visualization?
A: Presenting data graphically for easy understanding.
Q: What is Decision Making in ML?
A: Using data insights to guide managerial actions.

Q: Applications of ML in real life?
A: Image processing, robotics, data mining, video games, text analysis, healthcare.

Q: What is an AI Agent?
A: A tool that performs tasks autonomously by perceiving, reasoning, and acting on its environment.
Q: Key functions of AI agents?
A: Monitoring, responsive actions, reasoning, problem solving, inference learning, outcome analysis.

Q: What are Simple Reflex Agents?
A: React to immediate perceptions (e.g., thermostat).
Q: What are Model‑Based Reflex Agents?
A: Use internal models for partially observable environments (e.g., robot vacuum).
Q: What are Goal‑Based Agents?
A: Make decisions to achieve specific goals (e.g., GPS navigation).
Q: What are Utility‑Based Agents?
A: Maximize performance using utility functions (e.g., investment AI).
Q: What are Learning Agents?
A: Improve performance over time (e.g., recommendation engines).
Q: What are Multi‑Agent Systems (MAS)?
A: Multiple agents working together (e.g., smart city infrastructure).
Q: What are Hierarchical Agents?
A: Organized in layers with specific roles (e.g., manufacturing plant management AI).

Q: Example of AI agent in business?
A: Website optimization using Google Analytics → monitors traffic, identifies weak pages, suggests improvements, performs A/B testing, refines strategies.

Q: Future trends of AI agents?
A: AI‑enabled customer experience, automation & robotics, generative AI, AI‑assisted decision making, ethical AI.

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