Artificial Intelligence has grown to have a significant impact on the world. With large amounts of data being generated by different applications and sources, machine learning systems can learn from the test data and perform intelligent tasks. Artificial Intelligence is the field of computer science that deals with imparting the decisive ability and thinking the ability to machines. Artificial Intelligence is thus a blend of computer science, data analytics, and pure mathematics.
Machine learning becomes an integral part of Artificial Intelligence, and it only deals with the first part, the process of learning from input data. Artificial Intelligence and its benefits have never ceased to amaze us. Less than a decade after breaking the Nazi encryption machine Enigma and helping the Allied Forces win World War II, mathematician Alan Turing changed history a second time with a simple question: “Can machines think?”
Turing’s paper “Computing Machinery and Intelligence” (1950), and its subsequent Turing Test, established the fundamental goal and vision of artificial intelligence. What is artificial intelligence At its core, AI is the branch of computer science that aims to answer Turing’s question in the affirmative. It is the endeavor to replicate or simulate human intelligence in machines.
The expansive goal of artificial intelligence has given rise to many questions and debates. So much so, that no singular definition of the field is universally accepted. The major limitation in defining AI as simply “building machines that are intelligent” is that it doesn’t actually explain what artificial intelligence is? What makes a machine intelligent? In their groundbreaking textbook Artificial Intelligence: A Modern Approach, authors Stuart Russell and Peter Norvig approach the question by unifying their work around the theme of intelligent agents in machines. With this in mind, AI is “the study of agents that receive percepts from the environment and perform actions.”
Artificial intelligence algorithms can be broadly classified as:
1. Classification Algorithms
Classification algorithms are part of supervised learning. These algorithms are used to divide the subjected variable into different classes and then predict the class for a given input. For example, classification algorithms can be used to classify emails as spam or not. Let’s discuss some of the commonly used classification algorithms.
a) Naïve Bayes
Naïve Bayes algorithm works on Bayes theorem and takes a probabilistic approach, unlike other classification algorithms. The algorithm has a set of prior probabilities for each class. Once data is fed, the algorithm updates these probabilities to form something known as posterior probability. This comes useful when you need to predict whether the input belongs to a given list of classes or not.
b) Decision Tree
The decision tree algorithm is more of a flowchart like an algorithm where nodes represent the test on an input attribute and branches represent the outcome of the test.
c) Random Forest
Random forest works like a group of trees. The input data set is subdivided and fed into different decision trees. The average of outputs from all decision trees is considered. Random forests offer a more accurate classifier as compared to Decision tree algorithm.
d) Support Vector Machines
SVM is an algorithm that classifies data using a hyperplane, making sure that the distance between the hyperplane and support vectors is maximum.
e) K Nearest Neighbors
KNN algorithm uses a bunch of data points segregated into classes to predict the class of a new sample data point. It is called “lazy learning algorithm” as it is relatively short as compared to other algorithms.
2. Regression Algorithms
Regression algorithms are a popular algorithm under supervised machine learning algorithms. Regression algorithms can predict the output values based on input data points fed in the learning system. The main application of regression algorithms includes predicting stock market price, predicting weather, etc. The most common algorithms under this section are
a) Linear regression
It is used to measure genuine qualities by considering the consistent variables. It is the simplest of all regression algorithms but can be implemented only in cases of linear relationship or a linearly separable problem. The algorithm draws a straight line between data points called the best-fit line or regression line and is used to predict new values.
b) Lasso Regression
Lasso regression algorithm works by obtaining the subset of predictors that minimizes prediction error for a response variable. This is achieved by imposing a constraint on data points and allowing some of them to shrink to zero value.
c) Logistic Regression
Logistic regression is mainly used for binary classification. This method allows you to analyze a set of variables and predict a categorical outcome. Its primary applications include predicting customer lifetime value, house values, etc.
d) Multivariate Regression
This algorithm has to be used when there is more than one predictor variable. This algorithm is extensively used in retail sector product recommendation engines, where customers preferred products will depend on multiple factors like brand, quality, price, review etc.
e) Multiple Regression Algorithm
Multiple Regression Algorithm uses a combination of linear regression and non-linear regression algorithms taking multiple explanatory variables as inputs. The main applications include social science research, insurance claim genuineness, behavioral analysis, etc.
3. Clustering Algorithms
Clustering is the process of segregating and organizing the data points into groups based on similarities within members of the group. This is part of unsupervised learning. The main aim is to group similar items. For example, it can arrange all transactions of fraudulent nature together based on some properties in the transaction. Below are the most common clustering algorithms.
a) K-Means Clustering
It is the simplest unsupervised learning algorithm. The algorithm gathers similar data points together and then binds them together into a cluster. The clustering is done by calculating the centroid of the group of data points and then evaluating the distance of each data point from the centroid of the cluster. Based on the distance, the analyzed data point is then assigned to the closest cluster. ‘K’ in K-means stands for the number of clusters the data points are being grouped into.
b) Fuzzy C-means Algorithm
FCM algorithm works on probability. Each data point is considered to have a probability of belonging to another cluster. Data points don’t have an absolute membership over a particular cluster, and this is why the algorithm is called fuzzy.
c) Expectation-Maximization (EM) Algorithm
It is based on Gaussian distribution we learned in statistics. Data is pictured into a Gaussian distribution model to solve the problem. After assigning a probability, a point sample is calculated based on expectation and maximization equations.
d) Hierarchical Clustering Algorithm
These algorithms sort clusters hierarchical order after learning the data points and making similarity observations. It can be of two types
Divisive clustering, for a top-down approach.
Agglomerative clustering, for a bottom-up approach
AI has startled the world multiple times and has a lot of applications in the real world to solve its complex problems. We hope this article has shed some light on the various Artificial Intelligence algorithms and their broad classifications. Algorithms are chosen based on the need and the nature of the data points we have.
Algorithms have their advantages and disadvantages in terms of accuracy, performance and processing time. These are just a few algorithms.