Artificial Intelligence Algorithms Interview Preparation Guide Download PDF
AI Algorithms frequently Asked Questions by expert members with experience in Artificial Intelligence Algorithms. So get preparation for the AI Algorithms job interview
57 Artificial Intelligence Algorithms Questions and Answers:
1 :: What is Back propagation in Neural Networks?
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.
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.
2 :: What is Naive Bayes Algorithm?
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.
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.
3 :: what is software cycle? Give a diagrammatic representation?
Explain what is software cycle
4 :: What are the minimum requirements for statr testing?
Explain minimum requirements for statr testing
5 :: List the types of linked list with aid of diagram?
List down the types of linked list with aid of diagram yourself
6 :: What is a cybernetics in artificial intelligence algorithms?
A cybernetics is the study of communication between human and machine.
7 :: What is the goal of artificial intelligence algorithms?
The scientific goal of artificial intelligence is to explain various sorts of intelligence.
8 :: When does an algoritham complete?
An Algorithm is complete if It terminates with a solution when one exists.
9 :: Which is true regarding BFS in artificial intelligence algorithms?
Regarding BFS, the entire tree so far been generated must be stored in BFS.
10 :: What is a heuristic function in artificial intelligence algorithms?
Heuristic function is a function that maps from problem state descriptions to measures of desirability.
11 :: The traveling salesman problem involves n cities with paths connecting the cities. The time taken for traversing through all the cities, without knowing in advance what is the length of tour?
The traveling salesman problem involves n cities with paths connecting the cities. The time taken for traversing through all the cities, without knowing in advance the length of a minimum tour, is O(n!).
12 :: When an algorithm A is admissible in artificial intelligence algorithms?
An algorithm A is admissible if It is guaranteed to return an optimal solution when one exists.
13 :: What is idempotency law in artificial intelligence algorithms?
Idempotency Law in artificial intelligence algorithms is P V P = P.
14 :: What is knowledge in artificial intelligence algorithms?
Knowledge in artificial intelligence algorithms may be declarative and procedural.
15 :: What do we mean by simulated annealing in artificial intelligence?
a) Returns an optimal solution when there is a proper cooling schedule
b) Returns an optimal solution when there is no proper cooling schedule
c) It will not return an optimal solution when there is a proper cooling schedule
d) None of the mentioned
a) Returns an optimal solution when there is a proper cooling schedule.
Explanation:
Refer to the definitions of annealing search
Explanation:
Refer to the definitions of annealing search
16 :: Which of the following algorithm is generally used CSP search algorithm?
a) Breadth-first search algorithm
b) Depth-first search algorithm
c) Hill-climbing search algorithm
d) None of the mentioned
b) Depth-first search algorithm
Explanation:
Provides backtrack facility.
Explanation:
Provides backtrack facility.
17 :: When do we call the states are safely explored?
a) A goal state is unreachable from any state
b) A goal state is denied access
c) A goal state is reachable from every state
d) None of the mentioned
c) A goal state is reachable from every state
19 :: Which search algorithm will use limited amount of memory?
a) RBFS
b) SMA*
c) Hill-climbing search algorithm
d) Both a & b
d) Both a & b
Explanation:
RBFE and SMA* will solve any kind of problem that A* can't by using limited amount of memory.
Explanation:
RBFE and SMA* will solve any kind of problem that A* can't by using limited amount of memory.
20 :: Constraint propagation technique actually modifies the CSP problem.
a) True
b) False
a) True
Explanation:
Constraints are propagated towards goal node, modifying actual problem.
Explanation:
Constraints are propagated towards goal node, modifying actual problem.