Source: Robot Network
In fact, artificial intelligence has been a part of our lives for a long time. However, for many people, artificial intelligence is still a relatively “profound” technology,but no matter how profound the technology is, it starts from basic principles.There are 10 major algorithms in the field of artificial intelligence, which are quite simple in principle, discovered and applied long ago, and you may have learned them in middle school; they are also very common in daily life.
1.Linear Regression
Linear Regression（Linear Regression）） is probably the most popular machine learning algorithm. Linear regression aims to find a straight line that fits the data points in a scatter plot as closely as possible. It attempts to represent the independent variable (x value) and the dependent variable (y value) by fitting the line equation to the data. This line can then be used to predict future values!The most commonly used technique in this algorithm is Least Squares. This method calculates the best fit line to minimize the vertical distance from each data point to the line. The total distance is the sum of the squares of the vertical distances of all data points (the green line). The idea is to fit the model by minimizing this squared error or distance.
For example, simple linear regression has one independent variable (x-axis) and one dependent variable (y-axis), such as predicting the price increase of houses next year or the sales of new products in the next quarter. It doesn’t sound difficult, but the challenge of the linear regression algorithm lies not in deriving the predicted values, but in how to be more accurate. For that potentially very subtle number, many engineers have exhausted their youth and hair.
2.Logistic Regression
Logistic Regression（Logistic regression） is similar to linear regression, but the output of logistic regression can only have two values. While linear regression predicts an open numerical value, logistic regression is more like answering a yes or no question.The range of Y values in the logistic function is from 0 to 1, which represents a probability value. The logistic function typically has an S-shape, dividing the chart into two regions, making it suitable for classification tasks.
For instance, the logistic regression curve above shows the relationship between the probability of passing an exam and study time, which can be used to predict whether a student can pass the exam. Logistic regression is often used by e-commerce or food delivery platforms to predict users’ purchasing preferences for categories.
3.Decision Trees
If linear and logistic regression are tasks that finish in one round, thenDecision Trees（Decision Trees） represent a multi-step action, also used in regression and classification tasks, but the scenarios are usually more complex and specific. For example, a teacher facing a class of students needs to determine who the good students are. If the simple judgment is that students who score 90 or above are good, it seems too blunt and cannot rely solely on scores. For students scoring below 90, we can discuss aspects like homework, attendance, and questioning separately.
The above is an illustration of a decision tree, where each branching circle is called a node. At each node, we ask questions about the data based on available features. The left and right branches represent possible answers. The final nodes (i.e., leaf nodes) correspond to a predicted value. The importance of each feature is determined by a top-down approach. The higher the node, the more important its attribute. For example, the teacher in the above example considers attendance to be more important than homework, so the attendance node is higher, and of course, the score node is higher.
4.Naive Bayes
Naive Bayes（Naive Bayes） is based on Bayes’ theorem, which involves two conditional relationships. It measures the probability of each class and the conditional probability of each class given the value of x. This algorithm is used for classification problems, yielding a binary “yes/no” result. Take a look at the equation below.
The Naive Bayes classifier is a popular statistical technique, with classic applications inspam filtering. Of course, I bet that 80% of people won’t understand the above paragraph. (The 80% figure is just my guess, but experiential intuition is a form of Bayesian calculation.) To explain Bayes’ theorem in non-technical terms, it calculates the probability of A occurring given B, to deduce the probability of B occurring given A. For example, if a kitten likes you, there is a a% chance it will roll over in front of you; what is the probability that the kitten likes you given that it rolls over in front of you? Of course, solving problems this way is like shooting in the dark, so we need to introduce other data, such as the kitten liking you has a b% chance of cuddling and a c% chance of purring. So how do we know the probability of the kitten liking you? We can calculate it from the probabilities of rolling over, cuddling, and purring using Bayes’ theorem.
5.Support Vector Machine
Support Vector Machine（Support Vector Machine, SVM） is a supervised algorithm used for classification problems. The support vector machine tries to draw two lines between data points, maximizing the margin between them. To do this, we plot data items as points in n-dimensional space, where n is the number of input features. Based on this, the support vector machine finds an optimal boundary, called a hyperplane, which best separates the possible outputs by class labels. The distance between the hyperplane and the nearest class points is called the margin. The optimal hyperplane has the largest margin, allowing for classification of points while maximizing the distance between the nearest data points and the two classes.
Thus, the problem the support vector machine seeks to solve is how to separate a bunch of data. Its main application scenarios includecharacter recognition,facial recognition,text classification, and various other recognitions.
6.K-Nearest Neighbors (KNN)
K-Nearest Neighbors (KNN) is very simple. KNN classifies objects by searching for the K most similar instances in the entire training set, i.e., K neighbors, and assigns a common output variable to all these K instances. The choice of K is crucial: a smaller value may yield a lot of noise and inaccurate results, while a larger value is impractical. It is most commonly used for classification, but also applicable to regression problems. The distance used to evaluate the similarity between instances can be Euclidean distance, Manhattan distance, or Minkowski distance. Euclidean distance is the ordinary straight-line distance between two points. It is actually the square root of the sum of the squares of the differences in point coordinates.
KNN classification exampleKNN is simple in theory, easy to implement, and can be used fortext classification,pattern recognition,clustering analysis, and more.
7.K-Means
K-Means（K-means） is a clustering algorithm that classifies data sets. For example, this algorithm can be used to group users based on purchasing history. It finds K clusters in the data set. K-Means is used in unsupervised learning, so we only need to use the training data X and the number of clusters K we want to identify. The algorithm iteratively assigns each data point to one of the K groups based on each data point’s features. It selects K points for each K-cluster (called centroids). Based on similarity, new data points are added to the cluster with the nearest centroid. This process continues until the centroids stop changing.
In real life, K-Means plays an important role in fraud detection and is widely used in the fields of automotive, health insurance, and insurance fraud detection.
8.Random Forest
Random Forest（Random Forest） is a very popular ensemble machine learning algorithm. The basic idea of this algorithm is that the opinions of many people are more accurate than an individual’s opinion. In Random Forest, we use an ensemble of decision trees (see Decision Trees).
(a) During training, each decision tree is built based on bootstrap samples of the training set.(b) During classification, the decision for the input instance is made based on majority voting.Random Forest has a wide range of applications, frommarketing tohealthcare insurance, and can be used for modeling marketing simulations, analyzing customer acquisition, retention, and churn, as well as predicting disease risk and patient susceptibility.
9.Dimensionality Reduction
Due to the large amount of data we can capture today, machine learning problems become more complex. This means that training is extremely slow, and it is difficult to find a good solution. This issue is often referred to as the “Curse of Dimensionality“.Dimensionality Reduction（Dimensionality reduction） attempts to solve this problem by combining specific features into higher-level features without losing the most important information. Principal Component Analysis (PCA) is the most popular dimensionality reduction technique.Principal Component Analysis reduces the dimensionality of the data set by compressing it into low-dimensional lines or hyperplanes/subspaces while preserving the significant features of the original data as much as possible.
Dimensionality reduction can be exemplified by approximating all data points to a straight line.
10.Artificial Neural Networks (ANN)
Artificial Neural Networks（Artificial Neural Networks, ANN） can handle large and complex machine learning tasks. Neural networks are essentially a set of interconnected layers composed of nodes with weights, known as neurons. Between the input and output layers, we can insert multiple hidden layers. The artificial neural network uses two hidden layers. Additionally, deep learning needs to be addressed.The working principle of artificial neural networks is similar to the structure of the brain. A group of neurons is assigned random weights to determine how the neurons process the input data. By training the neural network on the input data, it learns the relationship between inputs and outputs. During the training phase, the system has access to the correct answers.If the network cannot accurately identify the input, the system will adjust the weights. After sufficient training, it will consistently recognize the correct patterns.
Each circular node represents an artificial neuron, and the arrows indicate the connections from the output of one artificial neuron to the input of another.Image recognition, is a well-known application of neural networks.Now, you have a basic understanding of the most popular artificial intelligence algorithms and some awareness of their practical applications.
