Machine Learning Coursera Quiz Answers | Week 1

All Weeks Machine Learning Coursera Quiz Answers

Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome.

Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. Many researchers also think it is the best way to make progress towards human-level AI. In this class, you will learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself.

More importantly, you’ll learn about not only the theoretical underpinnings of learning but also gain the practical know-how needed to quickly and powerfully apply these techniques to new problems. Finally, you’ll learn about some of Silicon Valley’s best practices in innovation as it pertains to machine learning and AI.

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Machine Learning Coursera Quiz Answers

Machine Learning Week 01 Quiz Answers

Introduction Quiz Answers

Q1. 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 probability of it correctly predicting a future date’s weather.
  • The weather prediction task.
  • The process of the algorithm examining a large amount of historical weather data.
  • None of these.

Q1. 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 weather prediction task.
  • None of these.
  • The probability of it correctly predicting a future date’s weather.
  • The process of the algorithm examines a large amount of historical weather data.

Q2. The amount of rain that falls in a day is usually measured in either millimeter (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?

  • Regression
  • Classification

Q3. 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?

  • Classification
  • Regression

Q4. 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.

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

Q5. Which of these is a reasonable definition of machine learning?

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

Q6. Suppose you are working on weather prediction, and use a learning algorithm to predict tomorrow’s temperature (in degrees Centigrade/Fahrenheit).
Would you treat this as a classification or a regression problem?

  • Regression
  • Classification

Q7. 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
  • Classification

Q8. Suppose you are working on stock market prediction, and you would like to predict the price of a particular stock tomorrow (measured in dollars). You want to use a learning algorithm for this.
Would you treat this as a classification or a regression problem?

  • Regression
  • Classification

Q9. 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?

  • Regression
  • Classification

Q10. 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.

  • Given historical data of children’s ages and heights, predict children’s height as a function of their age.
  • 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).
  • 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”.
  • Examine a large collection of emails that are known to be spam email, to discover if there are sub-types of spam mail.

Linear Regression with One Variable Quiz Answers

Q1. Consider the problem of predicting how well a student does in her second year of college/university, given how well she did in her first year. Specifically, let x be equal to the number of “A” grades (including A-. A and A+ grades) that a student receives in their first year of college (freshmen year). We would like to predict the value of y, which we define as the number of “A” grades they get in their second year (sophomore year).
Here each row is one training example. Recall that in linear regression, our hypothesis is 

 to denote the number of training examples.

For the training set given above (note that this training set may also be referenced in other questions in this quiz), what is the value of m? In the box below, please enter your answer (which should be a number between 0 and 10).

  • 4

Q2. Many substances that can burn (such as gasoline and alcohol) have a chemical structure based on carbon atoms; for this reason, they are called hydrocarbons. A chemist wants to understand how the number of carbon atoms in a molecule affects how much energy is released when that molecule combusts (meaning that it is burned). The chemist obtains the dataset below. In the column on the right, “kJ/mol” is the unit measuring the amount of energy released.

  •  = −569.6,  = 530.9
  •   = −1780.0,  = −530.9
  •   = −569.6,  = −530.9
  •   = −1780.0,  = 530.9

Q3. For this question, assume that we are using the training set from Q1.
Recall our definition of the cost function was  What is? In the box below, please enter your answer (Simplify fractions to decimals when entering an answer, and ‘.’ as the decimal delimiter e.g., 1.5).

  • 0.5

Q4. In the given figure, the cost function J(\theta_0,\theta_1)J(θ0​,θ1​) has been plotted against \theta_0θ0​ and \theta_1θ1​, as shown in ‘Plot 2’. The contour plot for the same cost function is given in ‘Plot 1’. Based on the figure, choose the correct options (check all that apply).

  • If we start from point B, gradient descent with a well-chosen learning rate will eventually help us reach at or near point C, as the value of cost function J(\theta_0,\theta_1)J(θ0​,θ1​) is minimum at point C.
  • If we start from point B, gradient descent with a well-chosen learning rate will eventually help us reach at or near point A, as the value of cost function J(\theta_0,\theta_1)J(θ0​,θ1​) is maximum at point A.
  • If we start from point B, gradient descent with a well-chosen learning rate will eventually help us reach at or near point A, as the value of cost function J(\theta_0,\theta_1)J(θ0​,θ1​) is minimum at A.
  • Point P (The global minimum of plot 2) corresponds to point C of Plot 1.
  • Point P (the global minimum of plot 2) corresponds to point A of Plot 1.

Q5. Suppose that for some linear regression problem (say, predicting housing prices as in the lecture), we have some training set, and for our training set we managed to find some \theta_0θ0​, \theta_1θ1​ such that J(\theta_0, \theta_1)=0J(θ0​,θ1​)=0.

Which of the statements below must then be true? (Check all that apply.)

  • Gradient descent is likely to get stuck at a local minimum and fail to find the global minimum.
  • For this to be true, we must have \theta_0 = 0θ0​=0 and \theta_1 = 0θ1​=0 so that h_\theta(x) = 0​(x)=0
  • Our training set can be fit perfectly by a straight line, i.e., all of our training examples lie perfectly on some straight line.
  • For this to be true, we must have y^{(i)} = 0y(i)=0 for every value of i = 1, 2, \ldots, mi=1,2,…,m.

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