QMB3302 FINAL EXAM {LATEST 2026 -
2027 update} QUESTIONS AND ANSWERS
100% CORRECT
All the the nodes prior to the output nodes essentially 'guess' at the correct weights.
Then the algorithm checks to see if the initial guess is correct (usually not). When it is
wrong... - correct answer It tries again (runs another epoch)
Neural networks are an unsupervised technique, because there is no target variable.
(True or False) - correct answer False
When viewing a diagram of a neural network there are several layers. The input layer:
- correct answer Are te Xs, or inputs from your data
When viewing a diagram of a neural network there are several layers. The Output
layer: - correct answer Are the Ys (The target variable you are interested in)
,When viewing a diagram of a neural network there are several layers. The hidden
layer: - correct answer Something you dont see, here there is some computation to
transform X into the Y
NLP stands for - correct answer natural language processing
Tokenization, as defined in the lecture, is - correct answer A computer turning letters
and/or words into something it can read and understand, like numbers
Recommenders come in many flavors. 2 of the most common, often used together and
discussed in the lecture are: - correct answer User based and Item based
Imagine you have a dataset with 2 columns, both filled with continuous numbers. You
believe the first column is a predictor of the second column. Which of the model
, approaches below could work when building a model? - correct answer Random
forests, regression, decision trees (Maybe not the BEST solution, Decision Trees have
some problems like overfitting that we discussed. )
Decision trees have a few problems, you should probably review those for the final
exam! The problem we talked about the most is: - correct answer Overfitting
We will start with the most familiar linear regression, a straight-line fit to data. A
straight-line fit is a model of the form
y=ax+b
Where a is commonly known as the - correct answer Slope
2027 update} QUESTIONS AND ANSWERS
100% CORRECT
All the the nodes prior to the output nodes essentially 'guess' at the correct weights.
Then the algorithm checks to see if the initial guess is correct (usually not). When it is
wrong... - correct answer It tries again (runs another epoch)
Neural networks are an unsupervised technique, because there is no target variable.
(True or False) - correct answer False
When viewing a diagram of a neural network there are several layers. The input layer:
- correct answer Are te Xs, or inputs from your data
When viewing a diagram of a neural network there are several layers. The Output
layer: - correct answer Are the Ys (The target variable you are interested in)
,When viewing a diagram of a neural network there are several layers. The hidden
layer: - correct answer Something you dont see, here there is some computation to
transform X into the Y
NLP stands for - correct answer natural language processing
Tokenization, as defined in the lecture, is - correct answer A computer turning letters
and/or words into something it can read and understand, like numbers
Recommenders come in many flavors. 2 of the most common, often used together and
discussed in the lecture are: - correct answer User based and Item based
Imagine you have a dataset with 2 columns, both filled with continuous numbers. You
believe the first column is a predictor of the second column. Which of the model
, approaches below could work when building a model? - correct answer Random
forests, regression, decision trees (Maybe not the BEST solution, Decision Trees have
some problems like overfitting that we discussed. )
Decision trees have a few problems, you should probably review those for the final
exam! The problem we talked about the most is: - correct answer Overfitting
We will start with the most familiar linear regression, a straight-line fit to data. A
straight-line fit is a model of the form
y=ax+b
Where a is commonly known as the - correct answer Slope
