Artificial Intelligence Algorithms Interview Preparation Guide

A back-propagation neural network is only practical in
certain situations. Following are some guidelines on when
you should use another approach:

Can you write down a flow chart or a formula that
accurately describes the problem? If so, then stick with a
traditional programming method.
Is there a simple piece of hardware or software that
already does what you want? If so, then the development
time for a NN might not be worth it.
Do you want the functionality to "evolve" in a direction
that is not pre-defined? If so, then consider using a
Genetic Algorithm (that's another topic!).
Do you have an easy way to generate a significant number of
input/output examples of the desired behavior? If not, then
you won't be able to train your NN to do anything.
Is the problem is very "discrete"? Can the correct answer
can be found in a look-up table of reasonable size? A look-
up table is much simpler and more accurate.
Are precise numeric output values required? NN's are not
good at giving precise numeric answers.
Conversely, here are some situations where a BP NN might be
a good idea:

A large amount of input/output data is available, but
you're not sure how to relate it to the output.
The problem appears to have overwhelming complexity, but
there is clearly a solution.
It is easy to create a number of examples of the correct
behavior.
The solution to the problem may change over time, within
the bounds of the given input and output parameters (i.e.,
today 2+2=4, but in the future we may find that 2+2=3.8).
Outputs can be "fuzzy", or non-numeric.

The Microsoft Naive Bayes algorithm is a classification
algorithm provided by Microsoft SQL Server Analysis Services
for use in predictive modeling. The name Naive Bayes derives
from the fact that the algorithm uses Bayes theorem but does
not take into account dependencies that may exist, and
therefore its assumptions are said to be naive.

This algorithm is less computationally intense than other
Microsoft algorithms, and therefore is useful for quickly
generating mining models to discover relationships between
input columns and predictable columns. You can use this
algorithm to do initial explorations of data, and then later
you can apply the results to create additional mining models
with other algorithms that are more computationally intense
and more accurate.

Explain what is software cycle

Explain minimum requirements for statr testing

List down the types of linked list with aid of diagram yourself

A cybernetics is the study of communication between human and machine.

The scientific goal of artificial intelligence is to explain various sorts of intelligence.

An Algorithm is complete if It terminates with a solution when one exists.

Regarding BFS, the entire tree so far been generated must be stored in BFS.

Heuristic function is a function that maps from problem state descriptions to measures of desirability.