Artificial Intelligence – The May 2007 Paper

Artificial Intelligence – Schema Survival Probability

 

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Artificial Intelligence – Unit 6 – Review Questions

Artificial Intelligence – Unit 6 – Data Mining

1. What is data mining? What is the difference between a normal query environment and data mining?
2. Discuss the problems and hurdles usually faced by data mining projects.
3. Define learning. Explain in brief the salient features of learning.
4. What is a self-learning computer system? Discuss.
5. Explain the empirical cycle of scientific research.
6. Explain the concepts of theory formation and theory falsification.
7. Write a short note on concept learning.
8. Explain the following terms:
   1. Classification accuracy
   2. Transparency
   3. Statistical significance
   4. Information content
   5. Complexity of the search space
9. Elaborate the hill-climbing search of the kangaroo in mist problem. What are the issues of relevance for learning algorithms?
10. What is a data warehouse? Elaborate the four rules governing the structure of a data warehouse.
11. Explain the concept of meta-data.
12. How are decision support systems designed? Explain in brief.
13. Write a short note on architecture and infrastructure for a data warehouse.
14. Explain the six stages of the Knowledge Discovery process.
15. Write a short note on data cleaning.
16. Explain the coding step in KDD.
17. Discuss various visualization techniques in KDD.
18. Write a short note on the application of Kohonen self-organizing maps.
19. Discuss the role of Genetic Algorithms in visualization.
20. What is knowledge? How is it different from information? What are the four different types of knowledge?
21. What are the rules for setting up a KDD environment?
22. Discuss the application of data mining in customer profiling.
23. How can KDD be used to discover foreign key relationships?

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MSc CS @ RDNC — Class of 2006 – 07

Striking a pose! The class of 06-07!

M.Sc. CS Students at the DCS, R.D.National College on 14 August 2006

The students of M.Sc. Part I and Part II are all smiles on the eve of course-completion on Monday.

Here’s wishing them the best in their subsequent courses and all future endeavors!

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Artificial Intelligence: Lecture on Monday 14 August 2006 @ 8:30 AM

Lecture on Monday 14 August 2006 at 8:30 AM.

Practicals

1 – Working of an Artificial Neuron
Problem Statement LyX Source

2 – Fuzzy Systems as Structured Numerical Estimators
Problem Statement LyX Source

3 – K Nearest Neighbors
Problem Statement LyX Source

4 – Roulette Wheel Selection
Problem Statement LyX Source

5 – Genetic Operator Mutation
Problem Statement LyX Source

6 – Genetic Operator Crossover
Problem Statement LyX Source

7 – Genetic Operators – Selection and Mutation
Problem Statement LyX Source

8 – Evolve a Word with Crossover
Problem Statement LyX Source

9 – Maximize Function using Selection and Mutation
Problem Statement LyX Source

10 – Maximize Function using Crossover
Problem Statement LyX Source

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