UBC CPSC 340 2019W1 MIDTERM EXAM Oct 17th, 2018 Instructor: Mark Schmidt TIME: 80 minutes We are providing a copy of this exam to help you prepare for the style of questions we may ask during the midterm and final. However, the solution file is meant for you alone and we do not give permission to share these solution files with anyone. Both distributing solution files to other people or using solution files provided to you by other people are considered academic misconduct. Please see UBC's policy on this topic if you are not familiar with it: http://www. calendar . ubc. ca/vancouver/index. cfm?tree=3, 54, 111, 959 http: //www. calendar. ubc. ca/vancouver/index. cfm?tree=3, 54, 111, 960 · Do not open the exam until you are directed to do so. . Once you open the exam, make sure that it contains this cover page plus 8 pages of exam questions. · One letter-size sheet (both sides) of notes is allowed. No other material or accessories may be used. . You may use either pen or pencil, but exams written in pencil may not be eligible for regrading. . Please be prepared to present, upon request, a student card for identification. . If you need more space, use the blank page at the end of the exam, and clearly indicate that your work continues there. . Most questions require a short answer. Work efficiently and avoid writing lengthy answers. . The exam uses the notation from class (n refers to the number of training examples, d is the number of features, and so on). · If anything is unclear or seems ambiguous, state your assumptions. Question: 1 2 3 4 5 6 7 Total Points: 10 15 6 6 8 6 9 60 Score:
CPSC 340 Midterm Exam Question 1. 6 pts (10 points) (a) Using the notation from class, give the size of the following quantities in terms of n, d, and t. And in one short sentence, describe what the notation represents in this course. For example, for y you could write "n x 1: label of training example i". i. Cij ii. xi iii. ¡ iv. ?i v. X vi. 4 pts (b) Which of the following methods are examples of supervised learning? Circle all that apply. i. Hierarchical clustering ii. KNN classification iii. k-medians iv. Linear regression v. Naive Bayes vi. Outlierness ratio vii. Random forests viii. Robust regression Page 1 of 8
Midterm Exam CPSC 340 Question 2. (15 points) 5 pts (a) Which of the following methods are non-parametric? Circle all that apply. i. Random forests with depth-20 random trees ii. k-means clustering using k = log(n). iii. Density-based clustering with € = 0.1, minNeighbours = 10. iv. Density-based clustering with € = 0.1, minNeighbours = log(n). v. Linear regression with degree p = 7 polynomial basis. 10 pts (b) Which of the following changes would typically reduce training error? Circle all that apply. Note: for regression and unsupervised models, assume we use the squared training error