Here is the worksheet on an efficient median-finding algorithm.
Clustering Sample Solutions for Parts 1 and 2
Assignment #3
Here is the full assignment version of Assignment #3. (Here is the LaTeX source. It has a “.txt” extension because blogs.ubc.ca doesn’t allow upload of “.tex” files.)
This is due Monday 5 Mar at 10PM. (As always, we will release the solution shortly afterward, here on the blog.)
Please submit it on GradeScope.
For reference, here are the collected quizzes and the collected quizzes with sample solutions.
Clustering Worksheet, Part 2
Protected: Midterm #1 solutions
Protected: Assignment #2 Sample Solution
Graph Play Worksheet Sample Solutions
Here are sample solutions to the graph play worksheet. Note that these solutions take one particular approach (particularly with respect to representation) that may be very different from both what you did personally or your lecture section did!
Clustering worksheet
Here’s one version of a worksheet on clustering images. We’ll work a bit more on this same problem for our next worksheet.
Patrice and Steve each use their own images for clustering. 🙂
PageRank BONUS worksheet
Here is a tutorial and bonus mini-assignment worth up to three bonus marks on the PageRank algorithm. It’s based on the material in today’s PageRank session. If you weren’t able to make the session, there should be enough information in the walkthrough for you to be able to take a stab at the questions anyway.
If you’re interested, here is a zip file of the MATLAB functions Susanne used in the session. (A warning that some of the plotting commands may not work as expected in Octave or older versions of MATLAB.)
We’ve put a totally optional submission target for this on GradeScope (should appear later today). It’s due on 2 March to give you plenty of time 🙂
PageRank Solution Notes
We don’t supply any specific solutions to the PageRank worksheet. The goal was to frame a famous problem in graphs yourself, in particular noticing that an ill-defined English notion like “biggest bigwig” can lead to various well-defined metrics that in turn lead to very different algorithms.