Machine Learning (ML) is the core of artificial intelligence, involving many fields such as statistics, system identification, approximation theory, neural networks, optimization theory, computer science, and brain science. It studies how computers can simulate or implement human learning behaviors to acquire new knowledge or skills, reorganizing existing knowledge structures to continuously improve their performance.
Compared to traditional machine learning, which improves system performance based on experience, modern machine learning more often improves system performance based on data. Data-driven machine learning is one of the important methods in modern intelligent technology, which seeks patterns from observational data (samples) and uses these patterns to predict future or unobservable data.
Mitchell provided a more formal definition in 1997: Assuming we use P (Performance) to evaluate a computer program’s performance on a certain task T (Task), if a program improves its performance on T by utilizing experience E (Experience), then we say that the program has learned E concerning T and P.
What Does Machine Learning Include
Machine learning can be categorized based on the form of learning into supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning. The distinction lies in that supervised learning requires a labeled sample set, unsupervised learning does not require a labeled sample set, semi-supervised learning requires a small number of labeled samples, while reinforcement learning requires a feedback mechanism.
Supervised learning uses a limited training dataset with labels to build a model through a certain learning strategy/method to label (classify) or map new data/instances. Supervised learning requires that the classification labels of the training samples are known; the higher the accuracy of the classification labels, the more representative the samples are, and the higher the accuracy of the learning model. Supervised learning has been widely used in fields such as natural language processing, information retrieval, text mining, handwriting recognition, and spam detection.
The input of supervised learning is a sample set with labeled classification tags; simply put, it is given a set of standard answers. Supervised learning learns a function from this sample set with given classification labels, so when new data arrives, it can predict the classification label of the new data based on this function.
In supervised learning, input data is referred to as “training data”, and each training data group has a clear identifier or result, such as classifying “spam” and “non-spam” in an anti-spam system.
When establishing a predictive model, supervised learning creates a learning process that compares the predicted results with the actual results of the “training data”, continuously adjusting the predictive model until the model’s predicted results reach an expected accuracy.
The most typical supervised learning algorithms include regression and classification.
Unsupervised learning describes the structure/pattern hidden in unmarked data using unlabeled limited data. Unsupervised learning does not require training samples and manual labeled data, making it easier to compress data storage, reduce computation, and speed up algorithms, while also avoiding classification errors caused by positive and negative sample shifts. It is mainly used in economic forecasting, anomaly detection, data mining, image processing, pattern recognition, etc., such as organizing large computer clusters, social network analysis, market segmentation, astronomical data analysis, etc.
Compared to supervised learning, unsupervised learning does not have pre-labeled classification tags in the sample set, meaning there are no pre-given standard answers. It does not tell the computer what to do but allows the computer to learn how to classify data on its own and then incentivizes the correct classification behavior.
In unsupervised learning, data is not specifically labeled, and the learning model aims to infer some intrinsic structure of the data. Common application scenarios include learning association rules and clustering. Common algorithms include the Apriori algorithm, KMeans algorithm, random forest, and principal component analysis.
3. Semi-Supervised Learning
Semi-supervised learning is between supervised and unsupervised learning, primarily solving the problem of training and classification using a small number of labeled samples and a large number of unlabeled samples to reduce labeling costs and improve learning capabilities.
In this learning method, some input data is labeled, while some is not. This learning model can be used for prediction, but it must first learn the intrinsic structure of the data to reasonably organize the data for prediction.
Application scenarios include classification and regression, with algorithms that extend common supervised learning algorithms. These algorithms first attempt to model the unlabeled data and then predict the labeled data based on that. Examples include graph inference algorithms or Laplacian SVM.
4. Reinforcement Learning
Reinforcement learning is the learning of mapping from the environment to behavior in intelligent systems to maximize the value of the reinforcement signal function. Due to the limited information provided by the external environment, reinforcement learning systems must learn from their own experiences.
The goal of reinforcement learning is to learn the mapping from the state of the environment to behavior so that the actions chosen by the agent can obtain the maximum reward from the environment, making the evaluation of the learning system by the external environment optimal in some sense. It has been successfully applied in fields such as robot control, autonomous driving, chess, and industrial control.
In this learning mode, input data serves as feedback to the model, unlike supervised models where input data is merely a way to check the model’s correctness. In reinforcement learning, input data directly feeds back to the model, which must make immediate adjustments. Common application scenarios include dynamic systems and robot control.
Common algorithms include Q-Learning and temporal difference learning.
Applications of Machine Learning
Image recognition is one of the most common applications of machine learning. It is used to recognize objects, people, places, and digital images. A popular use case of image recognition and face detection is automatic friend tagging suggestions: Facebook offers us the ability to automatically suggest friend tags. Whenever we upload photos with Facebook friends, we automatically receive tagging suggestions with names, and the technology behind this is machine learning’s face detection and recognition algorithms. It is based on a Facebook project called “Deep Face” responsible for recognizing faces and individuals in images.
When using various search software, we have the option to “search by voice”, which belongs to speech recognition, a popular application of machine learning.
Speech recognition is the process of converting voice commands into text, also known as “speech-to-text” or “computer speech recognition”. Currently, machine learning algorithms are widely used in various speech recognition applications. Baidu assistant and some voice input methods are using speech recognition technology to follow voice commands.
If we want to go to a new place, we rely on mobile maps, which show us the correct path of the shortest route and predict traffic conditions. It predicts traffic conditions in two ways: whether traffic is smooth, slow, or severely congested: real-time vehicle locations from map applications and sensors, and average time from the past few days. Every user of the mobile map helps the application improve. It collects information from users and sends it back to its database to enhance performance.
4. Product Recommendations
Machine learning is widely used by various e-commerce and entertainment companies such as JD.com and Taobao to recommend products to users. Whenever we search for a product on JD.com, we receive advertisements for the same product while browsing on the same browser, which is due to machine learning. Taobao uses various machine learning algorithms to understand users’ interests and recommend products based on customer interests. Similarly, when we shop on Taobao, we find recommendations for entertainment series, movies, etc., which are also completed with the help of machine learning.
One of the most exciting applications of machine learning is autonomous vehicles. Machine learning plays a crucial role in autonomous vehicles. The most popular car manufacturer, Tesla, is developing autonomous vehicles. It uses unsupervised learning methods to train car models to detect people and objects while driving. Domestic autonomous vehicles are also very popular, such as Shanghai Jiao Tong University’s use of autonomous vehicles for food delivery during the pandemic.
6. Spam and Malware Filtering
Whenever we receive a new email, it is automatically filtered into important mail, normal mail, and spam. We always receive an important email with a significant symbol in the inbox, while spam will also be present in the spam box. The technology behind this is machine learning. Here are some spam filters used by Gmail: content filters, header filters, general blacklist filters, rule-based filters, and permission filters. Some machine learning algorithms, such as multilayer perceptrons, decision trees, and naive Bayes classifiers, are used for email spam filtering and malware detection.
7. Virtual Personal Assistants
We have various virtual personal assistants, such as Cortana and Siri. As the name suggests, they can help us find information using voice commands. These assistants can assist us in various ways through our voice commands, such as playing music, calling someone, opening emails, scheduling appointments, etc. These virtual assistants use machine learning algorithms as an essential component. They record our voice commands, send them to cloud servers, and use ML algorithms to decode them and take appropriate actions.
8. Online Fraud Detection
Machine learning makes our online transactions secure by detecting fraudulent transactions. Whenever we conduct online transactions, fraudulent transactions can occur in various ways, such as fake accounts, fake IDs, and stealing money during transactions. To detect this, feedforward neural networks help us by checking whether it is a real or fraudulent transaction. For each real transaction, the output is converted into some hash values, which become the input for the next round. For each real transaction, there is a specific pattern that can change fraudulent transactions, so it detects them and makes our online transactions safer.
Machine learning is widely used in stock market trading. In the stock market, the risk of stock price fluctuations always exists, so long-term and short-term memory neural networks are used for predicting stock market trends.
In medical science, machine learning is used for disease diagnosis. With this, medical technology has developed rapidly and can establish 3D models that predict the exact location of lesions in the brain. Its image recognition technology helps easily identify brain tumors and other brain-related diseases.
11. Automatic Language Translation
Nowadays, if we visit a new place and do not know the language, it is not a problem at all, as machine learning also helps us by converting text into a language we know. Google’s GNMT (Google Neural Machine Translation) provides this function, which is a neural machine learning that translates text into a language we are familiar with, known as automatic translation. The technology behind automatic translation is a sequence-to-sequence learning algorithm used together with image recognition to translate text from one language to another.
Development of Machine Learning
We are in an era of breakthrough progress in AI: more complex neural networks accompanied by effective training data. The main problems faced by new machine learning algorithms are more complex, and the application fields of machine learning are developing from breadth to depth, raising higher requirements for model training and application. With the development of artificial intelligence, the theoretical basis of von Neumann-style finite state machines is becoming increasingly difficult to cope with the current requirements of layer depth in neural networks, all of which pose challenges to machine learning. The future of machine learning has two major breakthrough directions: one is improvement in algorithms, and the other is enhancement in computing power. How the future unfolds remains to be seen.
Machine learning involves many steps and processes, making it somewhat difficult for users to operate. The MatCloud+ platform also supports machine learning, making it relatively convenient to use. Especially, users do not need to download or install any software; they can conduct machine learning with just a browser. Here are simple operational instructions.
Additionally, this platform has machine learning templates, so users do not need to remember every step of the operation to achieve the processes mentioned above.
Today, we briefly introduced the applications and developments of material data machine learning. More content related to computational simulation and machine learning will be released in the future, so please stay tuned if you want to learn more.
Previous Recommendations:
Practical Word Skills in Thesis Writing! Recommended for Collection!
PhD Experience Sharing from 985 Universities: Publishing 8 SCI Papers in Half a Year from Scratch
Professor Yang Quanhong from Tianjin University AEM: Liquid Metal Remediation Strategy Achieves Ultra-Stable Lithium Storage in Micron Silicon Particles
Professor Guo Yuguo from the Institute of Chemistry, Chinese Academy of Sciences AM: Lithium Carbonate as a Protective Layer for High Nickel Cathode Materials Achieves Stable Improvement in Electrochemical Performance
The Antiferroelectric Tunnel Junction with a Large Off-On Ratio Can Really Exist
Professor Xia Dingguo from Peking University AM: Lithium-Rich Manganese-Based Cathode Materials Achieve Ultra-High Capacity Multi-Anion Redox
