Details of this Paper

COMP 9417 Machine Learning and Data Mining Exam




Name: Student number;11s1: COMP9417 Machine Learning and Data Mining;SAMPLE: Mid-session Examination;Your Name and Student number must appear at the head of this page.;Duration of the exam: 1 hour.;This examination has five questions. Answer all questions.;Total marks available in the exam: 50.;Multiple-choice questions require only one answer.;Show all working in your script book.;Paper is NOT to be retained by the candidate.;1 Please see overThis page intentionally left blank.;2 Please see overQuestion 1 [Total marks: 5];Well-posed Machine Learning problems;(a) [1 mark] What is required to define a well-posed learning problem?;(b) [3 marks] Here are two potential real-world application tasks for machine learning;1. a winery wishes to uncover relationships between records of the quantitative analyses of;its wines from the lab and some key subjective descriptions applied to its wine (e.g. dry;fruity, light, etc.);2. you want to predict students? marks in the final exam of COMP9417 given their marks from;the other assessable components in the course ? you may assume that the corresponding;data from previous years is available;Pick one of the tasks and state how you would define it as a well-posed machine learning problem;in terms of the above requirements.;(c) [1 mark] Suggest a learning algorithm for the problem you chose (give the name, and in;a sentence explain why it would be a good choice).;Question 2 [Total marks: 6];Concept Learning;(a) [3 marks] Write an algorithm called ?Find-G? to find a maximally-general consistent;hypothesis. You can assume the data will be noise-free and that the target concept is in the;hypothesis space.;(b) [3 marks] Outline the steps in a proof that Find-G will never fail to cover a positive;example in the training set.;3 Please see overQuestion 3 [Total marks: 18];Decision Tree Learning;(a) [3 marks] Describe the main steps in the basic decision tree learning algorithm.;The table below contains a sample S of ten examples. Each example is described using two;Boolean attributes A and B. Each is labelled (classified) by the target Boolean function.;Id A B Class;1 1 0 +;2 0 1 -;3 1 1 -;4 1 0 +;5 1 1 -;6 1 1 -;7 0 0 +;8 1 1 +;9 0 0 +;10 0 0 -;(b) [2 marks] What is the entropy of thse examples with respect to the given classification?;[Note: you must show how you got your answer using the standard formula.];This table gives approximate values of entropy for frequencies of positive examples in a two-class;sample.;Frequency of class ?+? in sample Entropy of sample;0.0 0.00;0.1 0.47;0.2 0.72;0.3 0.88;0.4 0.97;0.5 1.00;0.6 0.97;0.7 0.88;0.8 0.72;0.9 0.47;1.0 0.00;4 Please see over(c) [4 marks] What is the information gain of attribute A on sample S above?;(d) [4 marks] What is the information gain of attribute B on sample S above?;(e) [2 marks] Which would be chosen as the ?best? attribute by a decision tree learner using;the information gain splitting criterion? Why?;(f) [3 marks] Describe a method for overfitting-avoidance in decision tree learning.;Question 4 [Total marks: 10];Learning for Numeric Prediction;(a) Let the weights of a two-input perceptron be: w0 = 0.2, w1 = 0.5 and w2 = 0.5. Assuming;that x0 = 1, what is the output of the perceptron when;[i] [1 mark] x1 =?1 and x2 =?1?;[ii] [1 mark] x1 =?1 and x2 = 1?;Letting w0 =?0.2 and keeping x0 = 1, w1 = 0.5 and w2 = 0.5, what is the perceptron output;when;[iii] [1 mark] x1 = 1 and x2 =?1?;[iv] [1 mark] x1 = 1 and x2 = 1?;(b) [6 marks] Here is a regression tree with leaf nodes denoted A, B and C;X 5;X 9: C;This is the training set from which the regression tree was learned;5 Please see overX Class;1 8;3 11;4 8;6 3;7 6;8 2;9 5;11 12;12 15;14 15;Write down the output (class) values and number of instances that appear in each of the leaf;nodes A, B and C of the tree.;6 Please see overQuestion 5 [Total marks: 11];Neural and Tree Learning on Continuous Attributes;(a) [1 mark] In general, feedforward neural networks (multi-layer perceptrons) trained by;error back-propagation are;(i) fast to train, and fast to run on unseen examples;(ii) slow to train, and fast to run on unseen examples;(iii) fast to train, and slow to run on unseen examples;(iv) slow to train, and slow to run on unseen examples;In one sentence, explain your choice of answer.;Suppose you have a decision tree (DT) and a multi-layer perceptron (MLP) that have been;trained on data sampled from a two-class target function, with all attributes numeric. You can;think of both models as graphs whose edges are labelled with numbers: weights in the MLP and;threshold constants for attribute tests in the DT.;(b) [4 marks] Compare and contrast the roles of these numbers in the two models.;(c) [6 marks] Compare and contrast the methods of learning these numbers in the two models.;7 Please see over


Paper#30280 | Written in 18-Jul-2015

Price : $42