Explain Alpha–Beta pruning.

Alpha–Beta pruning is a search algorithm that tries to reduce the number of nodes that are searched by the minimax algorithm in the search tree. It can be applied to ‘n’ depths and can prune the entire subtrees and leaves.

What is model accuracy and model performance?

Model accuracy, a subset of model performance, is based on the model performance of an algorithm. Whereas, model performance is based on the datasets we feed as inputs to the algorithm.

What’s your favorite use case?

Just like research, you should be up to date on what’s going on in the industry. As such, if you’re asked about use cases, make sure that you have a few examples in mind that you can share. Whenever possible, bring up your personal experiences.

You can also share what’s happening in the industry. For example, if you’re interested in the use of AI in medical images, Health IT Analytics has some interesting use cases:

Detecting Fractures And Other Musculoskeletal Injuries
Aiding In The Diagnosis Neurological Diseases
Flagging Thoracic Complications And Conditions
Screening For Common Cancers

What are the hyper parameters of ANN?

Learning rate: The learning rate is how fast the network learns its parameters.
Momentum: It is a parameter that helps to come out of the local minima and smoothen the jumps while gradient descent.
Number of epochs: The number of times the entire training data is fed to the network while training is referred to as the number of epochs. We increase the number of epochs until the validation accuracy starts decreasing, even if the training accuracy is increasing (overfitting).

A* algorithm is based on which search method?

A* algorithm is based on best first search method, as it gives an idea of optimization and quick choose of path, and all characteristics lie in A* algorithm.

How would you describe ML to a non-technical person?

ML is geared toward pattern recognition. A great example of this is your Facebook newsfeed and Netflix’s recommendation engine.

In this scenario, ML algorithms observe patterns and learn from them. When you deploy an ML program, it will keep learning and improving with each attempt.

If the interviewer prods you to provide more real-world examples, you can list the following:

Amazon product recommendations

Fraud detection
Search ranking
Spam detection
Spell correction

What is overfitting? How is overfitting fixed?

Overfitting is a situation that occurs in statistical modeling or Machine Learning where the algorithm starts to over-analyze data, thereby receiving a lot of noise rather than useful information. This causes low bias but high variance, which is not a favorable outcome.

Overfitting can be prevented by using the below-mentioned methods:

Early stopping
Ensemble models
Cross-validation
Feature removal
Regularization

What is Artificial Intelligence?

Artificial Intelligence is an area of computer science that emphasizes the creation of intelligent machine that work and reacts like humans.

What is FOPL stands for and explain its role in Artificial Intelligence?

FOPL stands for First Order Predicate Logic, Predicate Logic provides

a) A language to express assertions about certain “World”

b) An inference system to deductive apparatus whereby we may draw conclusions from such assertion

c) A semantic based on set theory

List some applications of AI.

Natural language processing
Chatbots
Sentiment analysis
Sales prediction
Self-driving cars
Facial expression recognition
Image tagging