Page 178 - Ai Book - 10

9. Supervised Learning is categorized into Classification and Clustering.
10. In the AI Project Cycle, the Data Exploration stage comes after Data Modelling.

E. Very short answer type question.

1. What is the role of the “Where” block in the 4W’s Problem Canvas?
2. What is the main purpose of the Rule-Based Approach in Data Modelling?
3. Which stage in the AI Project Cycle involves finding unknown patterns in data?
4. What is the primary function of hidden layers in a Neural Network?

5. Which visualization technique is suitable for comparing primary data with respect to other data?
6. What is the primary goal of Clustering in Unsupervised Learning models?

7. How does Reinforcement Learning differ from Supervised Learning?
8. What does the term “Data Acquisition” involve in AI projects?
9. Which type of learning is associated with labeled datasets in AI models?
10. What is the aim of the Evaluation stage in the AI project life cycle?

F. Short answer type question.

1. How does Dimensionality Reduction contribute to solving complex problems in AI?
2. What are the two major components of a Rule-Based Artificial Intelligence model?
3. Which visualization technique is suitable for representing the frequency of continuous data?

4. Describe the primary function of Clustering in Unsupervised Learning models.
5. How does a Tree Diagram in data visualization contribute to AI systems?
6. What challenges are associated with Rule-Based Approach in AI systems?

7. What are the key advantages of Learning-Based Approach in AI models?
8. Explain the concept of Reinforcement Learning and its application in AI.

G. Long answer type question.
1. Explain the Sustainable Development Goals and their role in shaping AI projects. Provide an example
theme and its associated goal.
2. Describe the stages involved in the Data Modelling phase of the AI Project Cycle. How do Rule-Based
systems differ from Learning-Based systems in this context?

3. Explain the concept of Data Feature and its importance in the AI project life cycle. Provide a real-world
example illustrating relevant data features.

4. Detail the process of Dimensionality Reduction and its significance in machine learning. How does it
contribute to obtaining a better fit predictive model?
5. In the Data Acquisition stage, explain the sources of data and their importance in ensuring authentic and
high-quality data. Provide examples of reliable data sources for AI projects.
H. Application based questions.

1. Think about a scenario in your daily life where you collect information, such as keeping track of your daily
activities or chores. How would the concept of data exploration be useful in organising and understanding
this information? Provide specific examples.

52
52