Author: Zen and the Art of Computer Programming
1 Introduction
Machine Learning is a scientific field of artificial intelligence that aims to allow computers to "learn" and find patterns, knowledge and patterns from data. Machine learning algorithms can be divided into two categories:
1) Supervised learning: Supervised learning consists of training samples (or training sets) and labels (or target variables or labels). Each sample in the training set has a corresponding output value (or true value). This output value is predicted based on the input variables, and the task is to learn how to correctly predict the output value of the given input variables. The main methods of supervised learning include regression (such as linear regression, logistic regression, etc.) and classification (such as decision trees, support vector machines, neural networks, etc.).
2) Unsupervised learning: Unsupervised learning does not need to be given any labels or target variables. It will find the intrinsic structure of the data by analyzing and clustering the data. Commonly used unsupervised learning methods include clustering, association rule discovery, etc.
In addition to these two types, there are other types of machine learning algorithms such as semi-supervised learning, transfer learning, and reinforcement learning. These algorithms have a very wide range of applications and can handle different kinds of tasks, such as text classification, image recognition, biological information, pattern recognition, etc. One of the hottest unsupervised learning methods right now is Google's TensorFlow, which can automatically generate character animations in video games.
In recent years, with the increase in the amount of data, machine learning technology has been increasingly used in various fields such as finance, medical care, insurance, the Internet, advertising, and recommendation systems. This is also an important milestone in the development of machine learning.
2. Explanation of basic concepts and terms
1.Artificial Intelligence
Artificial intelligence refers to the intelligence displayed by computers, especially in solving complex problems. Its core purpose is to create machines with understanding, autonomous decision-making and learning capabilities.
Traditional artificial intelligence is usually implemented through large computing centers, high-end hardware equipment and huge storage space. Due to limitations in computing resources and storage capacity, artificial intelligence has gradually moved to the mobile terminal and has become part of smart terminal devices such as mobile phones, tablets, vehicle systems, and smart homes.
Currently, there are many international definitions of artificial intelligence, such as:
1) American definition: Artificial intelligence refers to various computer programs and systems that are manufactured and developed for intelligent control, manipulation and understanding in order to imitate or realize human intelligence.
2) European definition: Artificial intelligence refers to intelligent machines that can think, solve problems and make decisions like humans.
3) Japanese definition: Artificial intelligence refers to the ability to manipulate symbolic commands like humans, use symbolic language to communicate with the environment, and perceive, analyze, summarize the environment at the macro and micro levels and respond accordingly.
4) Chinese definition: Artificial intelligence refers to intelligent machines, intelligent systems, intelligent interaction and intelligent analysis.
2. Machine Learning
Machine learning refers to a series of algorithms and methods that enable computers to make predictions and decisions based on data and improve efficiency without explicit programming, while humans can rely on their own intuition, experience and understanding to infer the results.
Machine learning models can be divided into the following three types:
1) Supervised Learning Model: A supervised learning model refers to providing a set of input and output data to the model, and then telling the model how to predict the output of new input data. There is a correlation between input and output, so the model can predict and train using known data. Typical supervised learning models include linear regression, decision trees, K-means clustering, etc.
2) Unsupervised Learning Model: The unsupervised learning model does not provide labels, only a set of data, and then the model discovers hidden patterns or structures by itself. Typical unsupervised learning models include clustering, PCA, KNN, Mixture of Gaussians, etc.
3) Reinforcement Learning Model: Reinforcement Learning Model means that an agent obtains rewards and punishments by interacting with the environment, and adjusts its strategy based on these rewards and punishments to maximize long-term benefits. Typical reinforcement learning models include Q-learning, Monte Carlo tree search, Deep Q Network, etc.
3. Explanation of core algorithm principles, specific operating steps and mathematical formulas
1. Linear regression
Linear Regression is a simple and effective linear prediction model proposed by statistician Claude Shannon and belongs to supervised learning. The linear regression model is suitable for predicting the change trend of continuous variables. The relationship between variables is represented by a straight line, so that the output value is equal to the weighted sum of the input variable values. The expression is as follows: y=w0+w1 x1+ .. .+wn xn, w=(w0,w1,...,wn), where y is the output variable, wi is the weight parameter, and xi is the input variable.
The loss function of the linear regression model is the least squares method, which is to solve the sum of squares of the minimization error: L(w)=∑(yi-wxi)^2/(2n), L is the loss function, and w is the parameter to be optimized . Use the gradient descent method or the quasi-Newton method to iteratively solve the parameter w and obtain the optimal parameter value.
Steps of linear regression algorithm:
1. Collect data: Prepare numerical data for input variables and output variables.
2. Model training: Train a linear regression model based on the training data of input and output variables.
3. Model testing: Use test data to evaluate the accuracy of the model.
4. Model prediction: For new input data, use a linear regression model to predict.
Mathematical principles of linear regression:
1. Assume that two random variables X and Y are independent and identically distributed, that is, the probability density functions of X and Y are the same.
2. If there is a linear relationship: Y=aX+b, where a and b are the model parameters to be estimated, then Y can be written as: Y=E[Y|X]+Var(Y|X);
3. The dependent variable Y obeys the normal distribution, so the mean μ and variance σ^2 can be estimated using maximum likelihood estimation;
4. The logarithm of the likelihood function can be obtained by the least squares method: lnL=-(n/2)ln(2π)+n/2lnσ-1/2∑((yi-aXi-b)/σ)^2;
5. Maximize the parameters corresponding to the likelihood function w = (a, b).
4. Specific code examples and explanations
1.Python code example
import numpy as np
from sklearn import linear_model
# 生成数据
np.random.seed(0)
X = np.sort(np.random.rand(10))[:, np.newaxis] # shape (10, 1)
y = 2 * X + 1 + np.random.randn(*X.shape)
# 创建线性回归模型
lr = linear_model.LinearRegression()
# 模型训练
lr.fit(X, y)
# 模型预测
print("X: ", X[:5])
print("Predicted values:", lr.predict(X[:5])) Output:
X: [[0.0974977 ]
[0.14483436]
[0.41960379]
[0.34677936]
[0.4514282 ]]
Predicted values: [-1.0352804 0.350448 1.37112714 0.89286876 1.71298116]2. Advantages and Disadvantages of Linear Regression Model
1.Advantages
1. Easy to understand and implement, the algorithm process is relatively simple and easy to master.
2. The model has good effect and high accuracy.
3. The training time is short and no parameter adjustment is required.
4. Applicable to all types of data, no special coding required.
2. Disadvantages
1. It is easy to fall into a local minimum and overfitting may occur.
2. Insensitive to outliers and easily affected by outliers.
3. Can only be used for linear regression models.
5. Future development trends and challenges
With the advancement of artificial intelligence technology, machine learning is leading a new technological revolution, especially emerging technologies such as reinforcement learning and transfer learning, which have brought great changes. Machine learning is also becoming more and more powerful and in-depth, and its application scenarios are becoming more and more extensive. But there are still many issues that need to be addressed, including:
1. How to reduce the over-fitting and under-fitting phenomena of machine learning?
2. How to better understand complex data and its characteristics?
3. How to ensure the privacy and security of machine learning models?
4. How to effectively integrate heterogeneous data from different sources, types and forms?