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.
Which is not commonly used programming language for AI?
Perl language is not commonly used programming language for AI
What Is The Difference Between Strong Ai And Weak Ai?
Strong AI makes the bold claim that computers can be made to think on a level (at least) equal to humans. Weak AI simply states that some "thinking-like" features can be added to computers to make them more useful tools... and this has already started to happen (witness expert systems, drive-by-wire cars and speech recognition software). What does 'think' and 'thinking-like' mean? That's a matter of much debate.
What Is The Difference Between Classical Ai And Statistical Ai?
Statistical AI is more concerned with “inductive” thought like given a set of pattern, induce the trend etc. While, classical AI, on the other hand, is more concerned with “ deductive” thought given as a set of constraints, deduce a conclusion etc.
Which search method takes less memory?
The “depth first search” method takes less memory.
Which is the best way to go for Game playing problem?
Heuristic approach is the best way to go for game playing problem, as it will use the technique based on intelligent guesswork. For example, Chess between humans and computers as it will use brute force computation, looking at hundreds of thousands of positions.
What does a hybrid Bayesian network contain?
A hybrid Bayesian network contains both a discrete and continuous variables.
Deep learning imitates the way our brain works i.e. it learns from experiences. It uses the concepts of neural networks to solve complex problems.
What is a bidirectional search algorithm?
In a bidirectional search algorithm, the search begins in forward from the beginning state and in reverse from the objective state. The searches meet to identify a common state. The initial state is linked with the objective state in a reverse way. Each search is done just up to half of the aggregate way.
What is an iterative deepening depth-first search algorithm?
The repetitive search processes of level 1 and level 2 happen in this search. The search processes continue until the solution is found. Nodes are generated until a single goal node is created. Stack of nodes is saved.
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.
Fuzzy logic is a subset of AI; it is a way of encoding human learning for artificial processing. It is a form of many-valued logic. It is represented as IF-THEN rules.
First-order predicate logic is a collection of formal systems, where each statement is divided into a subject and a predicate. The predicate refers to only one subject, and it can either modify or define the properties of the subject.
Name a few Machine Learning algorithms you know.
Support vector machines
Naive Bayes, and so on
Naive Bayes Machine Learning algorithm is a powerful algorithm for predictive modeling. It is a set of algorithms with a common principle based on Bayes Theorem. The fundamental Naive Bayes assumption is that each feature makes an independent and equal contribution to the outcome.
List the extraction techniques used for dimensionality reduction.
Independent component analysis
Principal component analysis
Kernel-based principal component analysis
What is ensemble learning?
Ensemble learning is a computational technique in which classifiers or experts are strategically formed and combined. It is used to improve classification, prediction, function approximation, etc. of a model.
List the steps involved in Machine Learning.
Choosing an appropriate model
Training the dataset
A hash table is a data structure that is used to produce an associative array which is mostly used for database indexing.
What is regularization in Machine Learning?
Regularization comes into the picture when a model is either overfit or underfit. It is basically used to minimize the error in a dataset. A new piece of information is fit into the dataset to avoid fitting issues.