GT Students and VeriÕed
View the Proctoring System Requirements to ensure that your set-up will work. Note that
proctoring is only supported on MacOS and Windows machines. We recommend 2 GB of free
space on your machi
...
GT Students and VeriÕed
View the Proctoring System Requirements to ensure that your set-up will work. Note that
proctoring is only supported on MacOS and Windows machines. We recommend 2 GB of free
space on your machine, and a functioning Webcam is required. Your space should be clean,
no writing visible on walls or surfaces, and you should be alone in the room. Please make
sure that you have veriÕed your ID before taking the exam.
95 Minute Time Limit
Instructions
Work alone. Do not collaborate with or copy from anyone else.
You may use any of the following resources:
One sheet (both sides) of handwritten (not photocopied or scanned) notes
If any question seems ambiguous, use the most reasonable interpretation (i.e. don't be like
Calvin):
3/3/2020 GT Students and Verified | Midterm Quiz 1 | ISYE6501x Courseware | edX
https://courses.edx.org/courses/course-v1:GTx+ISYE6501x+1T2020/courseware/a8e7783f3b6d4b21bbf5720bb6f02a92/5b27328248f3436fac8492b2… 2/27
Good Luck!
Question 0 -- Practice with Drag & Drop
0 points possible (ungraded)
Keyboard Help
Some of the quiz questions are Drag-and-Drop. You'll need to drag one or more answers to a
location.
Some answers might not be used at all, and some answers will be used once. To get full
credit you might need to drag more than one answer to some locations, just one answer to
other locations, and some locations might not have any correct answers.
Please do this quick practice question. The question will give you feedback to make sure
you've done it correctly, but the real quiz questions will not.
x=1,y=7
x=2,y=3 x=1,y=43/3/2020 GT Students and Verified | Midterm Quiz 1 | ISYE6501x Courseware | edX
https://courses.edx.org/courses/course-v1:GTx+ISYE6501x+1T2020/courseware/a8e7783f3b6d4b21bbf5720bb6f02a92/5b27328248f3436fac8492b2… 3/27
FEEDBACK
Correctly placed 3 items.
Good work! You have completed this drag and drop problem. Note that: (1) There are two places you
could've put (x=2,y=3); either one would be correct. (2) One location (x+y=2) had nothing dragged to it.
Another location had two answers dragged to it. (3) One choice (x=1,y=7) was not dragged anywhere,
since it wasn't correct for anything.
You have used 1 of 10 attempts.
Reset
Submit
Show Answer
Question 1
9/13 points (graded)
Keyboard Help
Drag each of the 13 models/methods to one of the 5 categories of question it is commonly
used for, unless no correct category is listed for it. For models/methods that have more than
one correct category, choose any one correct category; for models/methods that have no
correct category listed, do not drag them.
x=1,y=6
CUSUM Principal component
analysis3/3/2020 GT Students and Verified | Midterm Quiz 1 | ISYE6501x Courseware | edX
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FEEDBACK
Correctly placed 8 items.
Misplaced 4 items.
Good work! You have completed this drag and drop problem.
Final attempt was used, highest score is 9.0
Submit You have used 1 of 1 attempts.
Reset
Show Answer
Support vector machine
k-means
ARIMA CART Exponential smoothing
k-nearest-neighbor Linear regression Random forest
Logistic regression
Cross validation
GARCH3/3/2020 GT Students and Verified | Midterm Quiz 1 | ISYE6501x Courseware | edX
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Answers are displayed within the problem
Question 2
3.0/3.0 points (graded)
Select all of the following models that are designed for use with attribute/feature data (i.e.,
not time-series data):
You have used 1 of 1 attempt
k-nearest-neighbor
Support vector machine
Random forest
GARCH
Logistic regression
Principal component analysis
Exponential smoothing
Linear regression
CUSUM
ARIMA
k-means
Submit
3/3/2020 GT Students and Verified | Midterm Quiz 1 | ISYE6501x Courseware | edX
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Information for Questions 3a, 3b, 3c
Figures A and B show the training data for a soft classiÕcation problem,
using two predictors (x and x ) to separate between black and white
points. The dashed lines are the classiÕers found using SVM. Figure A uses
a linear kernel, and Figure B uses a nonlinear kernel that required Õtting
16 parameter values.
Figure A Figure B
Question 3a
3.0/3.0 points (graded)
3a. Select all of the following statements that are true.
1 2
Figure A's classiÕer is based only on the value of x1 .
Figure A's classiÕer is more likely to be over-Õt than Figure B's classiÕer.
Figure A's classiÕer has a narrower margin than Figure B's classiÕer in the training
data.
Figure A's classiÕer incorrectly classiÕes exactly 4 white points as black in the training
data.3/3/2020 GT Students and Verified | Midterm Quiz 1 | ISYE6501x Courseware | edX
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Answers are displayed within the problem
Answers are displayed within the problem
You have used 1 of 1 attempt
Question 3b
2.25/3.0 points (graded)
3b. Select all of the following statements that are true.
You have used 1 of 1 attempt
Question 3c
3.0/3.0 points (graded)
Figure A shows that the black point (7.2,1.4) is an outlier.
Submit
Figure B's classiÕer is better than Figure A's classiÕer, because Figure B's classiÕer
classiÕes more of the training data correctly.
Figure B's classiÕer is more likely to be over-Õt than Figure A's classiÕer.
Figure B's classiÕer incorrectly classiÕes exactly 5 white points in the training data.
Figure B shows that the black point (7.2,1.4) is colored incorrectly; it should actually be
white.
Submit
3/3/2020 GT Students and Verified | Midterm Quiz 1 | ISYE6501x Courseware | edX
https://courses.edx.org/courses/course-v1:GTx+ISYE6501x+1T2020/courseware/a8e7783f3b6d4b21bbf5720bb6f02a92/5b27328248f3436fac8492b2… 8/27
Answers are displayed within the problem
3c. Select all of the following statements that are true.
You have used 1 of 1 attempt
Question 3d
0.99/3.0 points (graded)
In the soft classiÕcation SVM model where we select coeÞcients ... to minimize
3d. Select each of the following cases when we would want to increase the value of .
A new point at (1,1) would be classiÕed as white by Figure A's classiÕer.
A new point at (1,1) would be classiÕed as white by Figure B's classiÕer.
A new point at (1,1) would be classiÕed as white by a -nearest-neighbor algorithm for
.
k
1 ≤ k ≤ 10
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