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PreReq quiz for Machine Learning for Healthcare (6.S897, HST.956)
This quiz will not count toward your grade, but will be used by the course staff to check prerequisites (6.036/6.862 or 6.867 or 9.520/6.860 or 6.806/6.864 or 6.438 or 6.034) and to assess students' preparation for this class.
Please submit it as soon as possible -- and no later than Tuesday 2/5/19 at 11:59pm EST. We expect the quiz to take no more than 15-20 minutes.
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Email
*
Your email
Full Name (first, last)
*
Your answer
Which of these MIT machine learning classes have you taken?
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6.867
9.520/6.860
6.806/6.864
6.438
6.034
6.036
6.862
Other:
Required
Consider the following generative process describing the joint distribution p(Z,X): Z ~ Bernoulli(0.2), X ~ Gaussian(mu_Z, 0.5), where mu_0=3 and mu_1=5. Which of the following plots is the marginal distribution p(X)?
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(A)
(B)
(C)
(D)
Consider the following one-dimensional dataset for the next two questions. There are two classes (blue circles and red squares).
[references image above] Where is the decision boundary of a (hard margin) linear SVM classifier trained on the full dataset?
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-2.5
-1.75
-1.5
-1
0
[references image above] What is the leave-one-out cross-validation error (i.e. average fraction of misclassified points from 6-fold cross-validation) for a hard-margin linear SVM classifier on this data?
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0
1/6
1/3
1/2
1
Gradient descent will always converge to a global minimum, even for non-convex functions.
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True
False
Please match the four values of C to the four figures. For each row (i.e. figure), select the column which corresponds to the correct value of C.
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C=0.1
C=1
C=10
C=100
Figure a
Figure b
Figure c
Figure d
C=0.1
C=1
C=10
C=100
Figure a
Figure b
Figure c
Figure d
What would be the value of output z in the neural network below for the input x1=1 and x2=0 ?
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-3
-2
-1
0
1
2
3
Please self-report which tasks you would be comfortable doing in Python for a given .csv of movies, cast list, genre, and box office gross numbers:
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Open the .csv file and read its content into a data structure.
Find average box office gross for horror movies in 2019.
Plot the cast list length versus box office gross.
Find frequent bi-grams in movie titles.
Fit a scikit-learn LinearRegression model to predict box office gross using all of the other features.
Determine the best hyperparameter C for the model using cross-validation.
Determine which feature is the strongest individual predictor of box office gross.
Required
Are you comfortable using Keras, TensorFlow, or PyTorch?
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Yes
No
What is your registration status as student for this class?
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MIT undergrad
MIT grad student
Non-MIT registered student
Why do you want to take this class?
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Your answer
Send me a copy of my responses.
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