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Foundation Of Learning And Adaptive Systems Question Paper

Foundation Of Learning And Adaptive Systems

Course:Bachelor Of Science In Information Technology

Institution: Kca University question papers

Exam Year:2010

UNIVERSITY EXAMINATIONS: 2010/2011
THIRD YEAR STAGE EXAMINATION FOR THE DEGREE OF BACHELOR
OF SCIENCE IN INFORMATION TECHNOLOGY
BIT 3103: FOUNDATION OF LEARNING AND ADAPTIVE SYSTEMS
DATE: DECEMBER 2010 TIME: 2 HOURS
INSTRUCTIONS: Answer question ONE and any other TWO questions
QUESTION ONE
a) Briefly explain the meaning and the importance of the following terms in machine learning systems
(i) Instance space (2 Marks)
(ii) Entropy (2 Marks)
(iii)Information gain (2 Marks)
(iv) Target function (2 Marks)
b) State and explain two methods that are used to solve over fitting problem in decision trees.
(2 Marks)
c) Briefly explain the algorithm for clustering as used in learning and adaptive systems (4 Marks)
d) Briefly describe four situations when machine learning technology is used. (4Marks)
e) Describe any three applications of machine learning systems (3 Marks)
f) The following table shows training examples of chances of passing in the final exam given certain
behaviour.
2
StudiedHar
d
HoursSleptBefo
re
Breakfast Passed?
No 5 Eggs No
No 9 Eggs No
Yes 6 Eggs No
No 6 Bread No
Yes 9 Bread Yes
Yes 8 Eggs Yes
Yes 8 Cereal Yes
Yes 6 Cereal Yes
What is the initial entropy of ‘passed’ class (3 Marks)
g) Explain the meaning of the term ‘machine learning’ as used in learning and adaptive systems
intelligence (2 Marks)
h) Briefly explain four disciplines that contribute to machine learning field (4 Marks)
QUESTION TWO
a) Briefly explain the meaning of the following terms
i) concept . (2 Marks)
ii) Neural Networks (2 Marks)
iii) Chromosome (2 Marks)
b) State and explain four parts of a learning system.Use a diagram to illustrate your answers (6 Marks)
c) Describe five metrics for evaluating a learning algorithm (5 Marks)
d) Briefly explain when the process of computing centroids stops in k- means algorithm ( 2 Marks)
e) A neural network consists of four main parts. Explain each of these parts (4 Marks)
f) Briefly explain the meaning of the term ‘fitness score’ (2 Marks)
g) Consider the following database
Day sky airtemp humidity wind water forecast water sport
1 sunny warm normal strong warm same yes
2 sunny warm high strong warm same yes
3 rainy cold high strong warm change no
3
4 sunny warm high strong cool change yes
h) Given the above database, describe the most general and most specific hypothesis for playing
watersport. (4 Marks)
i) Differentiate between true and sample error (2 Marks)
QUESTION THREE
a) Briefly explain the following terms
i) neural network (2 Marks)
ii) Genetic algorithm (2 Marks)
iii) reinforcement learning (2 Marks)
b) Describe three motivations of learning and adaptive systems (3 Marks)
c) State and explain four reinforcement methods. Use examples to illustrate your answers (4 Marks)
d) briefly explain the criteria of stopping decision tree learning (2 Marks)
e) Briefly distinguish between rote learning and case based reasoning (2 Marks)
f) Describe three assumptions of case based reasoning (3 Marks)
QUESTION FOUR
a) Explain the meaning of the following terms
i) centroid (2 Marks)
ii) Curse-of-Dimensionality (2 Marks)
iii).Manhattan distance (2 Marks)
b) Describe the steps followed by genetic algorithm in evolutionary computation (4 Marks)
c) State and explain genetic algorithm operators that are used in evolutionary computation. Give one
example for each case (6 Marks)
d) Describe the steps followed by k-means algorithm in clustering (4 Marks)
4
QUESTION FIVE
a) Briefly explain the following terms.
i) Random variable ( 2 Marks)
ii) Concept-Learning ( 2 Marks)
iii) Associative mapping (2 Marks)
b) Briefly explain two important properties of Bayesian networks (4 Marks)
c) State and explain any three paradigms of machine learning (6 Marks)
d) Briefly explain any four steps of designing a machine learning systems (4 Marks)

Foundation Of Learning And Adaptive Systems Question Paper

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