2023 Name Student number 11s1 COMP9417 Machine Learning and Data Mining SAMPLE Mid session Examination April 5 2011 Your | Assignment Collections

Name: Student number: 11s1: COMP9417 Machine Learning and Data Mining SAMPLE: Mid-session Examination April 5, 2011 Your Name and Student number must appear at the head of this page. Duration of the exam: 1 hour. This examination has ve 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 over This page intentionally left blank. 2 Please see over Question 1 [Total marks: 5] Well-posed Machine Learning problems (a) [1 mark] What is required to dene 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 nal 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 dene 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 nd 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 over Question 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…