A) In which of the following situations will a collaborative filtering system be the most
appropriate learning algorithm (compared to linear or logistic regression)?
i. You own a clothing store that sells many styles and brands of jeans. You have collected
reviews of the different styles and brands from frequent shoppers, and you want to use
these reviews to offer those shoppers discounts on the jeans you think they are most
likely to purchase.
ii.
You manage an online bookstore, and you have book ratings from many users. You
want to learn to predict the expected sales volume (number of books sold) as a function
of the average rating of a book.
ii. You run an online bookstore and collect the ratings of many users. You want to use
this to identify what books are "similar" to each other (i.e., if one user likes a certain
book, what are other books that she might also like?)
iv.
You're an artist and hand-painting portraits for your clients. Each client gets a different
portrait (of themselves) and gives you 1-5-star rating feedback, and each client
purchases at most 1 portrait. You'd like to predict what rating your next customer will
give you.
B) For which of the following tasks might K-means clustering be a suitable algorithm? Select all
that apply.
i. Given a set of news articles from many different news websites, find out what are the
main topics covered.
i. Given many emails, you want to determine if they are Spam or Non-Spam emails.
iii. Given historical weather records, predict if tomorrow's weather will be sunny or rainy.
W. From the user usage patterns on a website, figure out what different groups of users
exist.
