Get premium membership and access revision papers, questions with answers as well as video lessons.

Got a question or eager to learn? Discover limitless learning on WhatsApp now - Start Now!

Foundations Of Learning And Adaptive Systems Question Paper

Foundations Of Learning And Adaptive Systems

Course:Bachelor Of Science In Information Technology

Institution: Kca University question papers

Exam Year:2011

UNIVERSITY EXAMINATIONS: 2010/2011
THIRD YEAR EXAMINATION FOR THE DEGREE OF BACHELOR OF
SCIENCE IN INFORMATION TECHNOLOGY
BIT 3103: FOUNDATIONS OF LEARNING AND ADAPTIVE SYSTEMS
DATE: APRIL 2011 TIME: 2 HOURS
INSTRUCTIONS: Answer question ONE and any other TWO questions
QUESTION ONE
a) Briefly explain the meaning and the importance in machine learning systems
i) Entropy (2 Marks)
ii) Information gain (2 Marks)
b) State and explain two strategies that can be used to avoid over fitting in decision trees. (4 Marks)
c) Distinguish between rote learning and case-based reasoning algorithms (2 Marks)
d) Distinguish between supervised learning and unsupervised learning (4Marks)
e) Describe any three applications of learning and adaptive systems in business enterprises (3 Marks)
f) Before the final exam on A.I, you decide to evaluate your chances at an "A" by building a decision
tree based on prior data. You have the following data items:
Attend
classes
No of lessons 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
2
i) What is the initial entropy of ‘passed’? (3 Marks)
ii).Which attribute would the decision tree building algorithm choose for the root of the tree? Justify
your answer. (2 Marks)
g) Explain the meaning of the term ‘neural network’ as used in artificial intelligence (2 Marks)
h) Briefly explain two methods of handling noise in rote learning algorithm (2 Marks)
i) Distinguish between feed forward and recurrent neural networks. Draw a diagram for each case
(4 Marks)
QUESTION TWO
a) Briefly explain the meaning of the following terms
i) Machine learning (2 Marks)
ii) Genetic algorithms (2 Marks)
iii) Survivor (2 Marks)
b) Describe four parts of a learning system. Use a diagram to illustrate your answers (6 Marks)
c) Explain any two reasons for implementing genetic algorithms. (2 Marks)
d) Describe the pseudo code of genetic algorithm (4 Marks)
e) Briefly explain when the process of computing centroids stops in k- means algorithm (2 Marks)
QUESTION THREE
Briefly explain the following terms
i) Reinforcement learning ( 2 Marks)
ii) Percepts ( 2 Marks)
iii) Case based reasoning ( 2 Marks)
b) Describe three motivations of reinforcement learning (3 Marks)
c) State and explain four reinforcement techniques. Use examples to illustrate your answers (4 Marks)
d) Reinforcement schedules may be used to decrease the probability that a response pattern in a subject
will extinguish. Explain using examples any four reinforcement schedules. ( 4 Marks)
e) Describe any three applications of reinforcement learning (3 Marks)
QUESTION FOUR
a) Briefly explain the following terms.
i) Concept (2 Marks)
ii) Concept-Learning ( 2 Marks)
iii) Training examples (2 Marks)
3
b) Briefly explain any four possible criteria for evaluating a learning algorithm (4 Marks)
c) Describe four situations that are appropriate Machine learning systems application ( 4 Marks)
d) Describe any three disciplines contributes to machine learning (3 Marks)
e) Briefly explain any three applications of case based reasoning in modern organizations
f) Describe three assumptions of case based reasoning ( 3 Marks).
QUESTION FIVE
a) Briefly explain the following concepts
i). inductive learning (2 Marks).
ii). Occam razor principle (2 Marks)
iii) Hypothesis (2 Marks)
b) Describe four challenges of implementing machine learning systems (4 Marks)
c) Briefly explain the algorithm for clustering as used in learning and adaptive systems (4 Marks)
d) Describe characteristics of a good machine learning algorithm (4 Marks)
e) Briefly explain the criteria of stopping decision tree learning (2 Marks)

Foundations Of Learning And Adaptive Systems Question Paper

More Question Papers

Popular Exams