Machine Learning

Machine learning (ML) employs algorithms and statistical models that enable computer systems to find patterns in massive amounts of data, and then uses a model that recognizes those patterns to make predictions or descriptions on new data.

In simplest terms, machine learning trains a machine to learn without being explicitly programmed how to do so. As a subset of AI, machine learning in its most elemental form uses algorithms to parse data, learn from it, and then make predictions or determinations about something in the real world.

In other words, machine learning uses algorithms to autonomously create models from data fed into a machine learning platform. Typical programmed or rule-based systems capture an expert's knowledge in programmed rules, but when data is changing, these rules can become difficult to update and maintain. Machine learning has the advantage of being able to learn from increasing volumes of data fed into the algorithms, and it can give data driven probabilistic predictions. This capability to utilize and apply highly complex algorithms to today’s big data applications quickly and effectively is a relatively new development.

Just about any discrete task that can be undertaken with a data-defined pattern or with a set of rules can be automated and therefore made far more efficient using machine learning. This allows companies to transform processes only possible previously by humans, including routing of customer service calls and reviewing resumes, among many others.

The performance of a machine learning system depends on the capability of some number of algorithms for turning a data set into a model. Different algorithms are needed for different problems and tasks, and solving them depends as well on the quality of the input data and power of the computing resources. 

Machine learning employs two main techniques that divide use of algorithms into different types: supervised, unsupervised, and a mix of these two. Supervised learning algorithms use labeled data, unsupervised learning algorithms find patterns in unlabeled data. Semi-supervised learning uses a mixture of labeled and unlabeled data. Reinforcement learning trains algorithms to maximize rewards based on feedback.

Supervised machine learning, also called predictive analytics, uses algorithms to train a model to find patterns in a dataset with labels and features. It then uses the trained model to predict the labels on a new dataset’s features.

Supervised learning can be further categorized into classification and regression.

Classification

Classification identifies which category an item belongs to based on labeled examples of known items. In the simple example below, logistic regression is used to estimate the probability of whether a credit card transaction is fraudulent or not (the label) based on features of transactions (transaction amount, time, and location of last transaction) known to be fraudulent or not.

Other examples of classification include:

• Spam detection
• Text sentiment analysis
• Predicting patient risk, sepsis, or cancer

Regression

Regression estimates the relationship between a target outcome label and one or more feature variables to predict a continuous numeric value. In the simple example below linear regression is used to estimate the house price (the label) based on the house size (the feature).

Other examples of regression include:

• Predicting the amount of fraud
• Predicting sales

Supervised learning algorithms include:

• Logistic regression
• Linear regression
• Support vector machines
• Decision trees
• Random forest
• Gradient boosting decision trees

Unsupervised learning, also called descriptive analytics, doesn’t have labeled data provided in advance, and can aid data scientists in finding previously unknown patterns in data. These algorithms attempt to ‘learn’ the inherent structure in the input data, discovering similarities, or regularities. Common unsupervised tasks include clustering and association.

Clustering

In clustering, an algorithm classifies inputs into categories by analyzing similarities between input examples. An example of clustering is a company that wants to segment its customers in order to better tailor products and offerings. Customers could be grouped on features such as demographics and purchase histories. Clustering with unsupervised learning is often combined with supervised learning in order to get more valuable results.

Other clustering examples include:

• Search results grouping
• Grouping similar patients
• Text categorization
• Anomaly detection (finding what is not similar, meaning the outliers from clusters)

Association learning

Association or frequent pattern mining finds frequent co-occurring associations (relationships, dependencies) in large sets of data items. An example of co-occurring associations is products that are often purchased together, such as the famous beer and diaper story. An analysis of behavior of grocery shoppers discovered that men who buy diapers often also buy beer.

Unsupervised learning algorithms include:

• K-means
• Latent Dirichlet allocation (LDA)
• Gaussian Mixture Model (GMM)
• Alternating least squares (ALS)
• FP-growth

The benefits of machine learning for business are varied and wide and include:

• Rapid analysis prediction and processing in a timely enough fashion allowing businesses to make rapid and data-informed decisions
• Facilitating accurate medical predictions and diagnoses by rapidly identifying high-risk patients, recommending a range of medicines, and predicting readmissions
• Simplifying time-intensive documentation in data entry by significantly reducing errors caused by data duplication and other inaccuracies while freeing up workers from the drudge of data entry
• Improving the precision of financial rules and models to boost portfolio management; enabling algorithmic trading, loan underwriting, and importantly boosting fraud detection
• Increasing efficiency of predictive maintenance by creating efficient, predictive maintenance plans
• Improving customer segmentation and lifetime value prediction, giving marketers invaluable information to optimize leads, maximizing web traffic, and boosting returns from mail and email campaigns.

Accelerated computing and ML are supercharging intelligent computing for healthcare. With one platform for imaging, genomics, patient monitoring, and drug discovery—deployed anywhere, from embedded to edge to every cloud—NVIDIA Clara^™ is enabling the healthcare industry to innovate and accelerate the journey to precision medicine.

Leading retailers are leveraging ML to reduce shrinkage, improve forecasting, automate warehouse logistics, determine in-store promotions and real-time pricing, enable customer personalization and recommendations, and deliver better shopping experiences—both in stores and online.

Understanding consumer behavior has never been more critical for retailers. To drive growth, intelligent recommendations are being used for personalized marketing. To improve revenue, online retailers are using GPU-powered machine learning (ML) and deep learning (DL) algorithms for faster, more accurate recommendation engines.​ Shoppers purchase and web action histories provide the data for a machine learning model’s analysis that yields the recommendations and supports the retailers’ upselling efforts. 

Financial institutions are adopting ML to deliver smarter, securer services. GPU-powered ML solutions can identify key insights in vast amounts of data to offload routine tasks from employees with automation, accelerate risk calculations and fraud detection, and enhance customer service with more accurate recommender systems.

NVIDIA provides pre-trained models and software solutions that greatly simplify ML applications. For instance, NVIDIA's Metropolis platform enables developers to build ML applications that improve retail inventory management, enhance loss prevention efforts, and simplify the checkout experience for consumers. 

As a practical example, Walmart uses NVIDIA's tech to manage employee workflow, and to ensure the freshness of meat and produce in certain stores. Likewise, BMW uses NVIDIA's edge AI solutions to automate optical inspections in its manufacturing facilities. And China Mobile, which operates the world's largest wireless network, uses NVIDIA's platform to deliver AI capabilities over 5G networks.