UNIVERSITY EXAMINATIONS: 2016/2017
EXAMINATION FOR THE DEGREE OF MASTER OF SCIENCE IN
DATA ANALYTICS
MDA 5203 MACHINE LEARNING
PART TIME/WEEKEND
DATE: DECEMBER, 2016 TIME: 2 HOURS
INSTRUCTIONS: Answer Question One & ANY OTHER TWO questions.
QUESTION ONE: [20 MARKS]
a) Explain the following concepts as used in neural networks:
i. Supervised learning
ii. Unsupervised learning
iii. Reinforcement learning
(3 Marks)
b) Evolutionary computation methods are based on Darwinian concept of “survival for the
fittest”. Using a suitable illustration diagram and example discuss a typical evolutionary
algorithm.
(7 Marks)
c) By first outlining the procedure for KNN decide the class for the new instance given in the
table below: Use K = 3
Explain the concept of K-Means and consequently determine the resulting clusters from
the dummy data given below: Use K=2 and let A and B be the initial point chosen. Use
Euclidean distance as the metric in this case.
QUESTION TWO: [15 MARKS]
Consider a two-class decision problem represented by the input-target pairs below. Train a
perceptron network to solve this problem using the perceptron learning rule.
Let the initial weight and bias be: W = [0,0] and b = 0. Use the Hardlim function as your
activation function, defined as:
QUESTION THREE [15 MARKS]
a) Decision trees are one of the main methods that use induction as a learning algorithm.
Explain the main concept behind induction.
(3 Marks)
b) Consider the following data set for a binary class problem.
Calculate the information gain (based on entropy) when splitting on A and B. Which
attribute would the decision tree induction algorithm choose?
(6 Marks)
ii. Calculate the gain in the Gini index when splitting on A and B. Which attribute
would the decision tree induction algorithm choose?
(6 Marks)
QUESTION FOUR [15 MARKS]
Using Naive Bayes determine the class for a new instance X = {A=T and B = F} based on the
table given in Qn 3(b).
(15 Marks
