Artificial Intelligence – Class 9

Class 9 introduces students to advanced Artificial Intelligence concepts. Students learn how AI systems are designed, trained, evaluated, and used to solve real-world problems. Logical thinking and technical understanding are strengthened.

What Students Will Learn:

1. Introduction to Artificial Intelligence Systems

Artificial Intelligence systems are designed to sense, think, learn, and act. These systems use data, algorithms, and computing power to solve complex problems efficiently.

2. Understanding Data in AI

Data is the foundation of AI. It can be structured or unstructured. AI systems require clean, accurate, and sufficient data to learn effectively and make reliable predictions.

3. Machine Learning Types

There are three main types of Machine Learning: Supervised Learning, Unsupervised Learning, and Reinforcement Learning. Each type is used for different problem-solving scenarios.

4. Training and Testing AI Models

AI models are trained using training data and evaluated using testing data. This helps measure the accuracy and performance of AI systems.

5. Introduction to Deep Learning

Deep Learning is a part of Machine Learning that uses multi-layered neural networks. It helps AI systems recognize complex patterns in images, speech, and videos.

6. AI Model Accuracy and Errors

AI accuracy shows how correctly a model predicts results. Errors occur due to poor data quality, bias, or incorrect algorithms. Improving data improves accuracy.

7. AI in Industries

AI is widely used in industries such as healthcare, agriculture, finance, transportation, and manufacturing. It increases efficiency, reduces cost, and improves decision-making.

8. Ethics, Bias, and Social Impact of AI

AI can affect society positively and negatively. Bias in AI can lead to unfair decisions. Ethical AI focuses on fairness, transparency, and accountability.

9. Cyber Safety and AI

AI systems must be protected from cyber threats. Cyber safety includes data protection, secure systems, and responsible online behavior.

10. AI Project Lifecycle

The AI project lifecycle includes problem identification, data collection, model building, testing, deployment, and improvement. This helps students understand how real AI projects are developed.

Learning Goal:

By the end of Class 9, students will understand AI system design, machine learning types, deep learning basics, ethics, and real-world AI applications.

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