Artificial Intelligence - The May 2007 Paper

Artificial Intelligence - Schema Survival Probability

 

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 2.5 License.

 

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?

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 2.5 License.

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!

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 2.5 License.

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

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 2.5 License.

Artificial Intelligence: Lecture on Thursday, 10 August 2006 @ 8:30 am

Lecture on Thursday, 10 August 2006 @ 8:30 am

Topics for the lecture:
1. Mathematical foundations for Genetic Algorithms
2. Computer Implementation of a Genetic Algorithm
Lecture will be followed by a practical session.
The Problem Statement is to be handwritten by the students. The pdf files list the required format.

The LyX Source zip archive contains the LyX source code for the procedure and solution documentation. Students should ensure:

1. Date is properly corrected to show the date on which the lab session was performed.
2. Roll No. if known should be typed in, else fill it in by hand later.
3. Screenshots of your processing should be present in place of the watermarked sample screenshots.

Lab 1: Working of an Artificial Neuron
Problem Statement
LyX Source

Lab 1 is due Friday, 11 August 2006, no exceptions. Earlier submissions are welcome!

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 2.5 License.

Artificial Intelligence: Annotated Syllabus Now Online

An annotated version of the syllabus with references to the relevant chapters and sections of the texts is now online.

This is merely indicative, and should not be considered to restrict the scope of the syllabus in any manner to the exclusion of relevant matter from the other texts in the list.

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 2.5 License.

MSc @ RDNC: Lectures Resume 24th July 2006

DC and AI Lectures will resume from Monday, 24 July 2006.

Be there at 8:30 am!

MSc @ RDNC: 20/07, 21/07, 22/07 : Lectures Cancelled

The DC and AI lectures for Thursday 20/07/2006, Friday 21/07/2006, and Saturday 22/07/2006  are cancelled.

Students are advised to ascertain their revised schedule from the Department.

Artificial Intelligence - May 2006 Question Paper