In computer science, Artificial Intelligence (AI) is the machine intelligence, which is demonstrated by machines, in contrast to the natural intelligence displayed by humans. Artificial Intelligence is also described cognitive functions that humans connect with the human mind to learn and solve problem. Contemporary machine capabilities usually classify AI as considering human speech, competing in strategic game systems, operating cars, routing content delivery networks, and military simulations.
Artificial Intelligence is a field of computer science wherein the cognitive functions of the human brain are studied and tried to be replicated on a machine/system. Artificial Intelligence is today widely used for various applications like computer vision, speech recognition, decision-making, perception, reasoning, cognitive capabilities, and so on.
Feedforward Neural Network
Convolutional Neural Network
Recurrent Neural Network(RNN) – Long Short Term Memory
Grid search trains the network for every combination by using the two set of hyperparameters, learning rate and the number of layers. Then evaluates the model by using Cross Validation techniques.
It randomly samples the search space and evaluates sets from a particular probability distribution. For example, instead of checking all 10,000 samples, randomly selected 100 parameters can be checked.
This includes fine-tuning the hyperparameters by enabling automated model tuning. The model used for approximating the objective function is called surrogate model (Gaussian Process). Bayesian Optimization uses Gaussian Process (GP) function to get posterior functions to make predictions based on prior functions.
Overfitting occurs when a statistical model or machine learning algorithm captures the noise of the data. This causes an algorithm to show low bias but high variance in the outcome.
Overfitting can be prevented by using the following methodologies:
Cross-validation: The idea behind cross-validation is to split the training data in order to generate multiple mini train-test splits. These splits can then be used to tune your model.
More training data: Feeding more data to the machine learning model can help in better analysis and classification. However, this does not always work.
Remove features: Many times, the data set contains irrelevant features or predictor variables that are not needed for analysis. Such features only increase the complexity of the model, thus leading to possibilities of data overfitting. Therefore, such redundant variables must be removed.
Early stopping: A machine learning model is trained iteratively, this allows us to check how well each iteration of the model performs. But after a certain number of iterations, the model’s performance starts to saturate. Further training will result in overfitting, thus one must know where to stop the training. This can be achieved by a mechanism called early stopping.
Regularization: Regularization can be done in n number of ways, the method will depend on the type of learner you’re implementing. For example, pruning is performed on decision trees, the dropout technique is used on neural networks and parameter tuning can also be applied to solve overfitting issues.
Use Ensemble models: Ensemble learning is a technique that is used to create multiple Machine Learning models, which are then combined to produce more accurate results. This is one of the best ways to prevent overfitting. An example is Random Forest, it uses an ensemble of decision trees to make more accurate predictions and to avoid overfitting.
Therefore, it is better to choose supervised classification for image classification in terms of accuracy.
Minimax is a recursive algorithm used to select an optimal move for a player assuming that the other player is also playing optimally.
A game can be defined as a search problem with the following components:
The Turing test is a method to test a machine’s ability to match the human-level intelligence. A machine is used to challenge human intelligence, and when it passes the test it is considered intelligent. Yet a machine could be viewed as intelligent without sufficiently knowing how to mimic a human.
A* is a computer algorithm that is extensively used for the purpose of finding the path or traversing a graph in order to find the most optimal route between various points called the nodes.
AI system uses game theory for enhancement; it requires more than one participant which narrows the field quite a bit. The two fundamental roles are as follows:
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.
A problem has to be solved in a sequential approach to attain the goal. The partial-order plan specifies all actions that need to be undertaken but specifies an order of the actions only when required.
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.
|Differentiation Based on||Parametric Model||Non-parametric Model|
|Benefits||Simple, fast, and less data||Flexibility, power, and performance|
|Limitations||Constrained, limited complexity, and poor fit||More data, slower, and overfitting|
|Features||A finite number of parameters to predict new data||Unbounded number of parameters|
|Algorithm||Logistic regression, linear discriminant analysis, perceptron, and Naive Bayes||K-nearest neighbors, decision trees like CART and C4.5, and support vector machines|
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.
In ‘Unification and Lifting’ the algorithm that takes two sentences and returns a unifier is ‘Unify’ algorithm.
State space search is the most straight forward approach for planning algorithm because it takes account of everything for finding a solution.
While staying within the HMM network, the additional state variables can be added to a temporal model.
In Artificial Intelligence, to extract the meaning from the group of sentences semantic analysis is used.
The process of determining the meaning of P*Q from P,Q and* is known as Compositional Semantics.
To solve temporal probabilistic reasoning, HMM (Hidden Markov Model) is used, independent of transition and sensor model.
In speech recognition, Acoustic signal is used to identify a sequence of words.
While creating Bayesian Network, the consequence between a node and its predecessors is that a node can be conditionally independent of its predecessors.
If a Bayesian Network is a representative of the joint distribution, then by summing all the relevant joint entries, it can solve any query.
Frames are a variant of semantic networks which is one of the popular ways of presenting non-procedural knowledge in an expert system. A frame which is an artificial data structure is used to divide knowledge into substructure by representing “stereotyped situations’. Scripts are similar to frames, except the values that fill the slots must be ordered. Scripts are used in natural language understanding systems to organize a knowledge base in terms of the situation that the system should understand.
In Artificial Intelligence to answer the probabilistic queries conditioned on one piece of evidence, Bayes rule can be used.
A heuristic function ranks alternatives, in search algorithms, at each branching step based on the available information to decide which branch to follow.
The production rule comprises of a set of rule and a sequence of steps.
Collaborative filtering can be described as a process of finding patterns from available information to build personalized recommendations. You can find collaborative filtering in action when you visit websites like Amazon and IMDB.
Also known as social filtering, this approach essentially makes suggestions based on the recommendations and preferences of other people who share similar interests.
When you’re dealing with a non-random sample, selection bias will occur due to flaws in the selection process. This happens when a subset of the data is consistently excluded because of a particular attribute. This exclusion will distort results and influence the statistical significance of the test.
Other types of biases include survivorship bias and undercoverage bias. It’s important to always consider and reduce such biases because you’ll want your smart algorithms to make accurate predictions based on the data.
The directions along which a particular linear transformation compresses, flips, or stretches is called eigenvalue. Eigenvectors are used to understand these linear transformations.
For example, to make better sense of the covariance of the covariance matrix, the eigenvector will help identify the direction in which the covariances are going. The eigenvalues will express the importance of each feature.
Eigenvalues and eigenvectors are both critical to computer vision and ML applications. The most popular of these is known as principal component analysis for dimensionality reduction (e.g., eigenfaces for face recognition).
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