# Artificial Intelligence Algorithms Interview Preparation Guide **Download PDF** Add New Question

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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.