Machine Learning Week 1, Quiz 1 - Introduction, Stanford University, Coursera

[x] Represents selected/correct answer
[ ] Not selected/incorrect answer

Question 1

A computer program is said to learn from experience E with
respect to some task T and some performance measure P if its
performance on T, as measured by P, improves with experience E.
Suppose we feed a learning algorithm a lot of historical weather
data, and have it learn to predict weather. WHat would be a
reasonable choice for P?

[ ] The weather prediction task.
[ ] The process of the algorithm examining a large amount of historical weather data.
[ ] None of these.
[x] The probability of it correctly predicting a future date's weather.

A computer program is said to learn from experience E with
respect to some task T and some performance measure P if its
performance on T, as measured by P, improves with experience E.
Suppose we feed a learning algorithm a lot of historical weather
data, and have it learn to predict weather. In this setting, what is T?

[ ] The probability of it correctly predicting a future date's weather.
[x] The weather prediction task.
[ ] The process of the algorithm examining a large amount of historical weather data.
[ ] None of these.

Question 2

The amount of rain that falls in a day is usually measured in
either millimiters (mm) or inches. Suppose you use a learning
algorithm to predict how much rain will fall tomorrow.
Would you treat this as a classification or a regression problem?

[ ] Classification
[x] Regression

Suppose you are working on weather prediction, and your weather
station makes one of three predictions for each day's weather:
Sunny, Cloudy or Rainy. You'd like to use a learning algorithm
to predict tomorrow's weather.
Would you treat this as a classification or a regression problem?

[ ] Regression
[x] Classification

Question 3

Suppose you are working on stock market prediction. You would like to predict whether or not
a certain company will win a patent infringement lawsuit (by training on data of companies
that had to defend against similar lawsuits). Would you treat this as a classification or a
regression problem?

[x] Classification
[ ] Regression

Suppose you are working on stock market prediction. You would like to predict whether or not
a certain company will declare bankruptcy within the next 7 days (by training on data of
similar companies that had previously been at risk of bankruptcy). Would you treat this as a
classification or a regression problem?

[x] Classification
[ ] Regression

Suppose you are working on stock market prediction, Typically
tens of millions of shares of Microsoft stock are traded
(i.e., bought/sold) each day. You would like to predict the
number of Microsoft shares that will be traded tomorrow.
Would you treat this as a classification or a regression problem?

[x] Regression
[ ] Classification

Question 4

Some of the problems below are best addressed using a supervised
learning algorithm, and the others with an unsupervised
learning algorithm. Which of the following would you apply
supervised learning to? (Select all that apply.) In each case, assume some appropriate
dataset is available for your algorithm to learn form.

[ ] Examine a large collection of emails that are known to be spam email, to discover if
there are sub-types of spam mail.
[ ] Take a collection of 1000 essays written on the US Economy, and find a way to
automatically group these essays into a small number of groups of essays that are
somehow "similar" or "related".
[x] Given 50 articles written by male authors, and 50 articles written by female authors,
learn to predict the gender of a new manuscript's author (when the identity of this
author is unknown).
[x] Given historical data of children's ages and heights, predict children's height as a
function of their age.

Some of the problems below are best addressed using a supervised learning algorithm, and
the others with an unsupervised learning algorithm. Which of the following would you apply
supervised learning to? (Select all that apply.) In each case, assume some appropriate dataset
is available for your algorithm to learn from.

[x] Given genetic (DNA) data from a person, predict the odds of him/her developing
diabetes over the next 10 years.
[x] Examine the statistics of two football teams, and predict which team will win
tomorrow's match (given historical data of teams' wins/losses to learn from).
[ ] Examine a large collection of emails that are known to be spam email, to discover if
there are sub-types of spam mail.
[ ] Take a collection of 1000 essays written on the US Economy, and find a way to
automatically group these essays into a small number of groups of essays that are
somehow "similar" or "related".

Question 5

Which of these is a reasonable definition of machine learning?

[ ] Machine learning learns from labeled data.
[ ] Machine learning is the science of programming computers.
[ ] Machine learning is the field of allowing robots to act intelligently.
[x] Machine learning is the field of study that gives computers the ability to learn
without being explicitly programmed.