Machine Learning week 1 quiz: Introduction

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 process of the algorithm examining a large amount of historical weather data.

The weather prediction task.

The probability of it correctly predicting a future date's weather.

None of these.

2. 

The amount of rain that falls in a day is usually measured in
either millimeters (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

Regression

3. 

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?

Regression

Classification

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 from.

Examine a large collection of emails that are known to be spam email, to discover if there are sub-types of spam mail.

Examine the statistics of two football teams, and predicting which team will win tomorrow's match (given historical data of teams' wins/losses to learn from).

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".

Given genetic (DNA) data from a person, predict the odds of him/her developing diabetes over the next 10 years.

5. 

Which of these is a reasonable definition of machine learning?

Machine learning is the field of allowing robots to act intelligently.

Machine learning is the science of programming computers.

Machine learning means from labeled data.

Machine learning is the field of study that gives computers the ability to learn without being explicitly programmed.