AI Midterm Lab Quiz 2_ Attempt review

.pdf

School

AMA Computer University *

*We aren’t endorsed by this school

Course

2333T

Subject

Computer Science

Date

Jun 9, 2024

Type

pdf

Pages

6

Uploaded by GrandGalaxyApe34

Started on Saturday, 8 June 2024, 7:02 AM State Finished Completed on Saturday, 8 June 2024, 7:20 AM Time taken 17 mins 29 secs Marks 16.00/20.00 Grade 80.00 out of 100.00 Question 1 Correct Mark 1.00 out of 1.00 Question 2 Correct Mark 1.00 out of 1.00 Question 3 Correct Mark 1.00 out of 1.00 What is the "M" step in the EM algorithm? a. The step where the expectation of the latent variables is calculated b. The step where the prediction accuracy of the model is calculated c. The step where the model parameters are updated d. The step where the likelihood of the model is maximized What is the main advantage of the Hebb rule? a. It is fast to converge b. It is able to handle nonlinear relationships c. It is easy to implement d. It is able to handle large datasets What is the Hebb rule? a. A rule used to calculate the output of a neural network b. A rule used to adjust the weights in a neural network c. A rule used to determine the structure of a neural network d. A rule used to determine the input to a neural network 08/06/2024, 10:20 Midterm Lab Quiz 2: Attempt review https://trimestral.amaesonline.com/2333/mod/quiz/review.php?attempt=117976&cmid=64470 1/6
Question 4 Correct Mark 1.00 out of 1.00 Question 5 Correct Mark 1.00 out of 1.00 Question 6 Correct Mark 1.00 out of 1.00 Question 7 Incorrect Mark 0.00 out of 1.00 What is the EM algorithm used to estimate in the "E" step? a. The prediction accuracy of the model b. The latent variables c. The model parameters d. The likelihood of the model What is the Naive Bayes classifier used for? a. To predict the value of a continuous variable b. To classify data into different categories based on certain features c. To predict the probability of an event occurring d. All of the above What is the equation for the Hebb rule? a. w(new) = w(old) + η(output - target)x(input) b. w(new) = w(old) + η(target - output)x(input) c. w(new) = w(old) + η(input - output)x(target) d. w(new) = w(old) + η(output)x(input) What is the advantage of using the Gaussian Naive Bayes classifier over other types of Naive Bayes classifiers? a. It is more accurate b. It is able to handle categorical features × c. It is able to handle continuous features d. It is faster to train and predict 08/06/2024, 10:20 Midterm Lab Quiz 2: Attempt review https://trimestral.amaesonline.com/2333/mod/quiz/review.php?attempt=117976&cmid=64470 2/6
Question 8 Correct Mark 1.00 out of 1.00 Question 9 Correct Mark 1.00 out of 1.00 Question 10 Incorrect Mark 0.00 out of 1.00 Question 11 Correct Mark 1.00 out of 1.00 What is the advantage of the Naive Bayes classifier over other classifiers? a. It is faster to train and predict b. It is more flexible c. It is more accurate d. It is able to handle large amounts of data What is the EM algorithm used to optimize in the "M" step? a. The likelihood of the model b. The prediction accuracy of the model c. The model parameters d. The latent variables What is the disadvantage of the Naive Bayes classifier? a. It is slower to train and predict b. It is less accurate c. It is inflexible d. It is unable to handle large amounts of data × What is the learning rule for a perceptron called? a. The Hebbian Rule b. The Perceptron Learning Algorithm c. The Backpropagation Algorithm d. The Delta Rule 08/06/2024, 10:20 Midterm Lab Quiz 2: Attempt review https://trimestral.amaesonline.com/2333/mod/quiz/review.php?attempt=117976&cmid=64470 3/6
Your preview ends here
Eager to read complete document? Join bartleby learn and gain access to the full version
  • Access to all documents
  • Unlimited textbook solutions
  • 24/7 expert homework help