Components and Stages (alias Al Project Cycle) — Chapter Summary
ICSE · Class 9 · Robotics & Artificial Intelligence
Summary of Components and Stages (alias Al Project Cycle) for ICSE Class 9 Robotics & Artificial Intelligence.
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Overview
The AI Project Cycle is the sequence of phases that a project goes through from its initiation to its closure. The cycle has five stages: Problem Scoping, Data Acquisition, Data Exploration, Modelling, and Evaluation. These stages give a clear framework for solving an AI problem, from identifying th
Key Concepts
Problem Scoping involves identifying a problem
Problem Scoping involves identifying a problem and having a vision to solve it. It narrows a broad theme into a clear problem statement using the 4Ws
The 4Ws Problem Canvas includes Who
The 4Ws Problem Canvas includes Who, What, Where, and Why. It helps define stakeholders, the nature of the problem, the context or location of the pro
Stakeholders are the people who face
Stakeholders are the people who face the problem and would be benefitted with the solution. They are identified in the Who block.
Data Acquisition is the stage
Data Acquisition is the stage of acquiring authentic, reliable, and relevant data for the AI project. Common ways of collecting data include surveys,
Training Data is the previous
Training Data is the previous or historical data used to train the machine. Testing Data is new data used after training to evaluate the model's predi
Learning Objectives
- Understand the five stages of the AI Project Cycle
- Learn how problem scoping helps narrow a broad theme into a clear problem statement
- Understand the 4Ws Problem Canvas: Who, What, Where, and Why
- Identify the role of training data and testing data in AI projects
- Understand how data is collected and why authentic data is important
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