QMB3302 FINAL EXAM QUESTIONS WITH COMPLETE
SOLUTIONS
regression analysis -- Answer ✔✔ An analytic technique where a series of input
variables are examined in relation to their corresponding output results in order to
develop a mathematical or statistical relationship.
regression analysis methods -- Answer ✔✔ linear
nonlinear
hierarchal clustering
Naive Bayes Classifier -- Answer ✔✔ predicts the probability of a certain outcome
based on prior occurrences of related events
fast and simple classification algos
are naive bayes suitable for high or low dimension databases? -- Answer ✔✔ high
generative model -- Answer ✔✔ an unsupervised model that predicts how likely a given
example is. eg, predicting the next word in the sentence
P(L | Features) -- Answer ✔✔ P ( features | L ) P(L) / P (features)
, neural network model -- Answer ✔✔ inspired by the way the brain stores and
processes info
input layer (x)
hidden layer
Output layer (Y)
hidden layer is -- Answer ✔✔ what should be applied to the x to equal the y
deep neural networks are -- Answer ✔✔ more than one hidden layer
hidden layers do what -- Answer ✔✔ apply weights to be chosen at random
amount of weights are equal to the amount of synapses
multiply weights by value of X
each of the synapses have -- Answer ✔✔ an activation function
this is the function we are trying to find
types of tasks for neural network -- Answer ✔✔ typically classification
choose the largest probability of an outcome
one of the most typical activation functions is -- Answer ✔✔ S(t) = 1 / ( 1+ e^-t)
SOLUTIONS
regression analysis -- Answer ✔✔ An analytic technique where a series of input
variables are examined in relation to their corresponding output results in order to
develop a mathematical or statistical relationship.
regression analysis methods -- Answer ✔✔ linear
nonlinear
hierarchal clustering
Naive Bayes Classifier -- Answer ✔✔ predicts the probability of a certain outcome
based on prior occurrences of related events
fast and simple classification algos
are naive bayes suitable for high or low dimension databases? -- Answer ✔✔ high
generative model -- Answer ✔✔ an unsupervised model that predicts how likely a given
example is. eg, predicting the next word in the sentence
P(L | Features) -- Answer ✔✔ P ( features | L ) P(L) / P (features)
, neural network model -- Answer ✔✔ inspired by the way the brain stores and
processes info
input layer (x)
hidden layer
Output layer (Y)
hidden layer is -- Answer ✔✔ what should be applied to the x to equal the y
deep neural networks are -- Answer ✔✔ more than one hidden layer
hidden layers do what -- Answer ✔✔ apply weights to be chosen at random
amount of weights are equal to the amount of synapses
multiply weights by value of X
each of the synapses have -- Answer ✔✔ an activation function
this is the function we are trying to find
types of tasks for neural network -- Answer ✔✔ typically classification
choose the largest probability of an outcome
one of the most typical activation functions is -- Answer ✔✔ S(t) = 1 / ( 1+ e^-t)
