UNIVERSITY EXAMINATIONS: 2011/2012
YEAR III EXAMINATION FOR THE BACHELOR OF SCIENCE IN
INFORMATION TECHNOLOGY
BIT 3103 FOUNDATIONS OF LEARNING AND ADAPTIVE SYSTEMS
DATE: APRIL 2012 TIME: 2 HOURS
INSTRUCTIONS: Answer Question One and Any other Two Questions
QUESTION ONE
a)Describe the meaning of the term ‘entropy’ and explain its significance in learning and adaptive
systems (3 Marks)
b) briefly explain four methods of selecting an attribute for partitioning the tree during decision
tree learning (4 Marks)
c) State and explain two strategies that can be used to avoid over fitting in decision trees. (4 Marks)
d) Describe an algorithm for clustering in the context of learning and adaptive systems (4 Marks)
e)Describe the term ‘target function’ as used in learning and adaptive systems (2 Marks)
f) Distinguish between supervised learning and unsupervised learning (4Marks)
g)Describe any three applications of learning and adaptive systems in business enterprises
(3 Marks)
h)Before the final exam on A.I, you decide to evaluate your chances of “Passed” by building a
decision tree based on prior data. You have the following data items:
i) What the initial entropy is of passed? (3 Marks)
j) Describe the meaning of the term ‘information gain’ and explain its importance in decision tree
learning algorithm (3 Marks)
QUESTION TWO
a)Briefly explain the meaning of the concept ‘Machine learning’ as used in learning and adaptive
systems (2 Marks)
b) Briefly explain two conditions for stopping computation of centroids stops in k- means
algorithm (2 Marks)
c) Describe any two algorithms of instance based learning in the context of learning and adaptive
systems (4 Marks)
d) Describe four main parts of a learning system. Use a diagram to illustrate your answers
(6 Marks)
e)State and explain four parts of a neural network as used in learning and adaptive systems
(4 Marks)
f) consider the following table
Compute city block distance between the two items (2 Marks)
QUESTION THREE
a)Briefly explain the following terms
i) percepts (2 Marks)
ii) Genetic algorithm (2 Marks)
iii) reinforcement learning (2 Marks)
b)briefly explain three advantages of reinforcement learning as used in learning and adaptive
systems ( 3 Marks)
c)State and explain four reinforcement techniques. Use examples to illustrate your answers
(4 Marks)
d)State and Explain four reinforcement schedules. Give one example for each case. (4 Marks)
e)Describe any three applications of reinforcement learning (3 Marks)
QUESTION FOUR
a)Explain the meaning of the following terms
i. Mutation (2 Marks)
ii. Concept description updater (2 Marks)
iii. fitness score (2 Marks)
b) Briefly explain four principles of genetic algorithms (4 Marks)
c) After an initial population is randomly generated, the algorithm evolves the through three
Operators. State and explain each of these operators. Give one example for each case (6 Marks)
d) Describe the steps followed by genetic algorithm in machine learning (4 Marks)
QUESTION FIVE
a)Briefly explain the following terms.
i. Concept (2 Marks)
ii. Concept-Learning (2 Marks)
iii. Training examples (2 Marks)
b) Briefly explain any four characteristics of a good learning algorithm (4 Marks)
c)Briefly explain four elements of reinforcement learning (4 Marks)
d) Describe any three disciplines contributes to machine learning (3 Marks)
e)Briefly explain any three applications of learning and adaptive systems in modern organizations
(3 Marks)
