question: A 6-sided die is rolled 15 times and the results are: side 1 comes up 0 times; side 2: 1 time; side 3: 2 times; side 4: 3 times; side 5: 4 times; side 6: 5 times. Based on these results, what is the probability of side 3 coming up when using Add-1 Smoothing?

options:
- 2.0/15
- 1.0/7
- 3.0/16
- 1.0/5


question: Which image data augmentation is most common for natural images?

options:
- random crop and horizontal flip
- random crop and vertical flip
- posterization
- dithering


question: You are reviewing papers for the World’s Fanciest Machine Learning Conference, and you see submissions with the following claims. Which ones would you consider accepting?

options:
- My method achieves a training error lower than all previous methods!
- My method achieves a test error lower than all previous methods! (Footnote: When regularisation parameter λ is chosen so as to minimise test error.)
- My method achieves a test error lower than all previous methods! (Footnote: When regularisation parameter λ is chosen so as to minimise cross-validaton error.)
- My method achieves a cross-validation error lower than all previous methods! (Footnote: When regularisation parameter λ is chosen so as to minimise cross-validaton error.)


question: To achieve an 0/1 loss estimate that is less than 1 percent of the true 0/1 loss (with probability 95%), according to Hoeffding's inequality the IID test set must have how many examples?

options:
- around 10 examples
- around 100 examples
- between 100 and 500 examples
- more than 1000 examples


question: Traditionally, when we have a real-valued input attribute during decision-tree learning we consider a binary split according to whether the attribute is above or below some threshold. Pat suggests that instead we should just have a multiway split with one branch for each of the distinct values of the attribute. From the list below choose the single biggest problem with Pat’s suggestion:

options:
- It is too computationally expensive.
- It would probably result in a decision tree that scores badly on the training set and a testset.
- It would probably result in a decision tree that scores well on the training set but badly on a testset.
- It would probably result in a decision tree that scores well on a testset but badly on a training set.