Machine Learning Definitions: A to Z Glossary Terms

The partial derivative of f with respect to x focuses only on

how x is changing and ignores all other variables in the equation. In other cases,

outliers aren’t mistakes; after all, values five standard deviations away

from the mean are rare but hardly impossible. For example, suppose that widget-price is a feature of a certain model. Assume that the mean widget-price is 7 Euros with a standard deviation

of 1 Euro. Examples containing a widget-price of 12 Euros or 2 Euros

would therefore be considered outliers because each of those prices is

five standard deviations from the mean. With numeric encoding, a model would interpret the raw numbers

mathematically and would try to train on those numbers.

• For instance, deep learning algorithms such as convolutional and recurrent neural networks are used in supervised, unsupervised and reinforcement learning tasks, based on the specific problem and data availability.
• For example, suppose you must train a model to predict employee

  stress level.

• The defining characteristic of a rule-based machine learning algorithm is the identification and utilization of a set of relational rules that collectively represent the knowledge captured by the system.
• The financial services industry is championing machine learning for its unique ability to speed up processes with a high rate of accuracy and success.

The sum of two convex functions (for example,

L2 loss + L1 regularization) is a convex function. Many variations of gradient descent

are guaranteed to find a point close to the minimum of a

strictly convex function. Similarly, many variations of

stochastic gradient descent have a high probability

(though, not a guarantee) of finding a point close to the minimum of a

strictly convex function. A strictly convex function has exactly one local minimum point, which

is also the global minimum point. However, some convex functions

(for example, straight lines) are not U-shaped. A floating-point feature with an infinite range of possible

values, such as temperature or weight.

Types of Machine Learning Tasks

ANI is considered “weak” AI, whereas the other two types are classified as “strong” AI. We define weak AI by its ability to complete a specific task, like winning a chess game or identifying a particular individual in a series of photos. Natural language processing and computer vision, which let companies automate tasks and underpin chatbots and virtual assistants such as Siri and Alexa, are examples of ANI.

A training approach in which the

algorithm chooses some of the data it learns from. Active learning

is particularly valuable when labeled examples

are scarce or expensive to obtain. Instead of blindly seeking a diverse

range of labeled examples, an active learning algorithm selectively seeks

the particular range of examples it needs for learning. Precision and

recall are usually more useful metrics

than accuracy for evaluating models trained on class-imbalanced datasets. Accelerator chips (or just accelerators, for short) can significantly

increase the speed and efficiency of training and inference tasks

compared to a general-purpose CPU. They are ideal for training

neural networks and similar computationally intensive tasks.

This need for transparency often results in a tradeoff between simplicity and accuracy. Although complex models can produce highly accurate predictions, explaining their outputs to a layperson — or even an expert — can be difficult. Algorithms trained on data sets that exclude certain populations or contain errors can lead to inaccurate models. Basing core enterprise processes on biased models can cause businesses regulatory and reputational harm.

Similarly, financial institutions use ML for fraud detection by monitoring transactions for suspicious behavior. Machine learning enables the personalization of products and services, enhancing customer experience. In e-commerce, ML algorithms analyze customer behavior and preferences to recommend products tailored to individual needs.

The key is identifying the right data sets from the start to help ensure that you use quality data to achieve the most substantial competitive advantage. You’ll also need to create a hybrid, AI-ready architecture that can successfully use data wherever it lives—on mainframes, data centers, in private and public clouds and at the edge. Whether you use AI applications based on ML or foundation models, AI can give your business a competitive advantage.

Personalization engines, powered by AI data mining, analyze vast amounts of customer data to create tailored product recommendations and marketing messages. For instance, Stitch Fix, an online personal styling service, uses AI to analyze customer preferences and feedback to curate personalized clothing selections. AI data mining techniques have also made waves in the eCommerce sector.

Computing the relative binding affinity of ligands based on a pairwise binding comparison network

Models or model components (such as an

embedding vector) that have been already been trained. Sometimes, you’ll feed pre-trained embedding vectors into a

neural network. Other times, your model will train the

embedding vectors themselves rather than rely on the pre-trained embeddings.

Various sectors of the economy are dealing with huge amounts of data available in different formats from disparate sources. The enormous amount of data, known as big data, is becoming easily available and accessible due to the progressive use of technology, specifically advanced computing capabilities and cloud storage. Companies and governments realize the huge insights that can be gained from tapping into big data but lack the resources and time required to comb through its wealth of information.

It also enables the use of large data sets, earning the title of scalable machine learning. That capability is exciting as we explore the use of unstructured data further, particularly since over 80% of an organization’s data is estimated to be unstructured (link resides outside ibm.com). To keep up with the pace of consumer expectations, companies are relying more heavily on machine learning algorithms to make things easier. You can see its application in social media (through object recognition in photos) or in talking directly to devices (such as Alexa or Siri). A validation set is a subset of the data used to evaluate the performance of a machine learning model during training and tune hyperparameters.

Careers in machine learning and AI

Feature sparsity refers to the sparsity of a feature vector;

model sparsity refers to the sparsity of the model weights. For example, a feature containing a single 1 value and a million 0 values is

sparse. In contrast, a dense feature has values that

are predominantly not zero or empty.

Applications of inductive logic programming today can be found in natural language processing and bioinformatics. Inductive logic programming is an area of research that makes use of both machine learning and logic programming. In ILP problems, the background knowledge that the program uses is remembered as a set of logical rules, which the program uses to derive its hypothesis for solving problems. One of the loss functions commonly used in

generative adversarial networks,

based on the earth mover’s distance between

the distribution of generated data and real data.

Detailed descriptions of the five ‘core’ variables used to create our streamlined models are presented in online supplemental table 5, while descriptions of all other variables are shown in online supplemental table 1. In an era where data is often called the new oil, artificial intelligence (AI) is the tool extracting valuable insights from vast digital reserves. AI-powered data mining, a technology at the intersection of machine learning and big data analytics, is reshaping industries and driving decision-making across the corporate landscape. An asset management firm may employ machine learning in its investment analysis and research area.

Reinforcement learning algorithms are used in autonomous vehicles or in learning to play a game against a human opponent. Semi-supervised machine learning uses both unlabeled and labeled data sets to train algorithms. Generally, during semi-supervised machine learning, algorithms are first fed a small amount of labeled data to help direct their development and then fed much larger quantities of unlabeled data to complete the model. For example, an algorithm may be fed a smaller quantity of labeled speech data and then trained on a much larger set of unlabeled speech data in order to create a machine learning model capable of speech recognition. In supervised machine learning, algorithms are trained on labeled data sets that include tags describing each piece of data.

Therefore, you

would find the mean and standard deviation of the MSE across all four rounds. For example, when building a classifier to identify wedding photos,

an engineer may use the presence of a white dress in a photo as a feature. However, white dresses have been customary only during certain eras and

in certain cultures. The production of plausible-seeming but factually incorrect output by a

generative AI model that purports to be making an

assertion about the real world. For example, a generative AI model that claims that Barack Obama died in 1865

is hallucinating. Gradient accumulation is useful when the batch size is

very large compared to the amount of available memory for training.

A hyperparameter that controls the degree of randomness

of a model’s output. Higher temperatures result in more random output,

while lower temperatures result in less random output. If the input

matrix is three-dimensional, the stride would also be three-dimensional. The term “sparse representation” confuses a lot of people because sparse

representation is itself not a sparse vector.

Each of those neurons contribute to the overall loss in different ways. Backpropagation determines whether to increase or decrease the weights. applied to particular neurons. In contrast, GAN-based image models are usually not auto-regressive. since they generate an image in a single forward-pass and not iteratively in. steps. However, certain image generation models are auto-regressive because. they generate an image in steps. You can foun additiona information about ai customer service and artificial intelligence and NLP. Gen AI has shone a light on machine learning, making traditional AI visible—and accessible—to the general public for the first time. The efflorescence of gen AI will only accelerate the adoption of broader machine learning and AI.

Clean and label the data, including replacing incorrect or missing data, reducing noise and removing ambiguity. This stage can also include enhancing and augmenting data and anonymizing personal data, depending on the data set. Convert the group’s knowledge of the business problem and project objectives into a suitable ML problem definition.

For example, the headline Red Tape Holds Up Skyscraper is a

crash blossom because an NLU model could interpret the headline literally or

figuratively. A fairness metric that checks whether a classifier

produces the same result for one individual as it does for another individual

who is identical to the first, except with respect to one or more

sensitive attributes. Evaluating a classifier for

counterfactual fairness is one method for surfacing potential sources of

bias in a model. The seminal paper on co-training is Combining Labeled and Unlabeled Data with

Co-Training by

Blum and Mitchell. A convolutional layer consists of a

series of convolutional operations, each acting on a different slice

of the input matrix.

The relevance scores determine how much the word’s final representation

incorporates the representations of other words. SavedModel

is a language-neutral, recoverable serialization format, which enables

higher-level systems and tools to produce, consume, and transform TensorFlow

models. R-squared is the square of the

Pearson correlation

coefficient

between the values that a model predicted and ground truth. A graph of true positive rate versus

false positive rate for different

classification thresholds in binary

classification. The term

ridge regularization is more frequently used in pure statistics

contexts, whereas L2 regularization is used more often

in machine learning. In reinforcement learning, the numerical result of taking an

action in a state, as defined by

the environment.

The central coordination process running on a host machine that sends and

receives data, results, programs, performance, and system health information

to the TPU workers. A programmable linear algebra accelerator with on-chip high bandwidth memory

that is optimized for machine learning workloads. A large gap between test loss and training loss or validation loss sometimes

suggests that you need to increase the

regularization rate. In reinforcement learning, the conditions that

determine when an episode ends, such as when the agent reaches

a certain state or exceeds a threshold number of state transitions.

Some models, however,

require sophisticated visualization to become interpretable. In-set conditions usually lead to more efficient decision trees than

conditions that test one-hot encoded features. For example, a line is a

hyperplane in two dimensions and a plane is a hyperplane in three dimensions. More typically in machine learning, a hyperplane is the boundary separating a

high-dimensional space. Kernel Support Vector Machines use

hyperplanes to separate positive classes from negative classes, often in a very

high-dimensional space. For example, hashing

could place baobab and red maple—two genetically dissimilar

species—into the same bucket.

In Deep Q-learning, a neural network that is a stable

approximation of the main neural network, where the main neural network

implements either a Q-function or a policy. Then, you can train the main network on the Q-values predicted by the target

network. Therefore, you prevent the feedback loop that occurs when the main

network trains on Q-values predicted by itself.

In customer service, chatbots powered by ML reduce the need for human agents, lowering operational expenses. In machine learning, weights are the parameters of a model that are adjusted during training to minimize the error or loss function. Model selection is choosing the best machine learning model from a set of candidate models based on their performance metrics and generalization ability. Reinforcement learning is a type of machine learning where an agent learns to interact with an environment by performing actions and receiving rewards or penalties based on its actions. The goal of reinforcement learning is to learn a policy, which is a mapping from states to actions, that maximizes the expected cumulative reward over time. From suggesting new shows on streaming services based on your viewing history to enabling self-driving cars to navigate safely, machine learning is behind these advancements.

In machine learning, an anonymization approach to protect any sensitive data

(for example, an individual’s personal information) included in a model’s

training set from being exposed. This approach ensures

that the model doesn’t learn or remember much about a specific

individual. This is accomplished by sampling and adding noise during model

training to obscure individual data points, mitigating the risk of exposing

sensitive training data.

To address this challenge, you need a solution that uses the latest advancements in generative AI to create a natural conversational experience. The solution should seamlessly integrate with your existing product catalog API and dynamically adapt the conversation flow based on the user’s responses, reducing the need for extensive coding. Traditional rule-based chatbots often struggle to handle the nuances and complexities of open-ended conversations, leading to frustrating experiences for users. Furthermore, manually coding all the possible conversation flows and product filtering logic is time-consuming and error-prone, especially as the product catalog grows.

Semisupervised learning combines elements of supervised learning and unsupervised learning, striking a balance between the former’s superior performance and the latter’s efficiency. Deep learning is a subfield of ML that focuses on models with multiple levels of neural networks, known as deep neural networks. These models can automatically learn and extract hierarchical features from data, making them effective for tasks such as image and speech recognition. Machine learning refers to the general use of algorithms and data to create autonomous or semi-autonomous machines.

After deploying, continuous monitoring and logging ensure that models are always updated with the latest data and performing optimally. Clear and thorough documentation is also important for debugging, knowledge transfer and maintainability. For ML projects, this includes https://chat.openai.com/ documenting data sets, model runs and code, with detailed descriptions of data sources, preprocessing steps, model architectures, hyperparameters and experiment results. Determine what data is necessary to build the model and assess its readiness for model ingestion.

In an image classification problem, an algorithm’s ability to successfully

classify images even when the position of objects within the image changes. For example, the algorithm can still identify a dog, whether it is in the

center of the frame or at the left end of the frame. A decoder transforms a sequence of input embeddings into a sequence of

output embeddings, possibly with a different length. A decoder also includes

N identical layers with three sub-layers, two of which are similar to the

encoder sub-layers.

Note that

the centroid of a cluster is typically not an example in the cluster. You can use the

Learning Interpretability Tool (LIT)

to interpret ML models. The ability to explain or to present an ML model’s reasoning in

understandable terms to a human. In decision forests, the difference between

a node’s entropy and the weighted (by number of examples)

sum of the entropy of its children nodes. Assuming that what is true for an individual is also true for everyone

in that group. The effects of group attribution bias can be exacerbated

if a convenience sampling

is used for data collection.

Instruction tuning involves training a model on a series

of instruction prompts, typically covering a wide

variety of tasks. The resulting instruction-tuned model then tends to

generate useful responses to zero-shot prompts

across a variety of tasks. If testers or raters consist of the machine learning developer’s friends,

family, or colleagues, then in-group bias may invalidate product testing

or the dataset.

Conversely,

if the retrained model performs equally well, then that feature was probably

not that important. Today, the need—and potential—for machine learning is greater than ever. The volume and complexity of data that is now being generated is far too vast for humans to reckon with. In the years since its widespread deployment, machine learning has had impact in a number of industries, including medical-imaging analysis and high-resolution weather forecasting. Similarly, standardized workflows and automation of repetitive tasks reduce the time and effort involved in moving models from development to production.

Categorizing based on Required Output

Unsupervised machine learning can find patterns or trends that people aren’t explicitly looking for. For example, an unsupervised machine learning program could look through online sales data and identify different machine learning definitions types of clients making purchases. Feature learning is motivated by the fact that machine learning tasks such as classification often require input that is mathematically and computationally convenient to process.

JAX is particularly well-suited for speeding up many machine learning tasks

by transforming the models and data into a form suitable for parallelism

across GPU and TPU accelerator chips. A Python-first configuration library that sets the

values of functions and classes without invasive code or infrastructure. In the case of Pax—and other ML codebases—these functions and

classes represent models and training

hyperparameters. In reinforcement learning, a DQN technique used to

reduce temporal correlations in training data. The agent

stores state transitions in a replay buffer, and then

samples transitions from the replay buffer to create training data. The mathematically remarkable part of an embedding vector is that similar

items have similar sets of floating-point numbers.

If no parameters are provided, it retrieves all the products in the table and returns the first 100 products. Before you create your agent, you need to set up the product database and API. We use an AWS CloudFormation template to create a DynamoDB table to store product information and a Lambda function to serve as the API for retrieving product details.

For example, a generative AI model can create sophisticated

essays or images. Unlike

a deep model, a generalized linear model cannot “learn new features.” A plot of both training loss and

validation loss as a function of the number of

iterations. A model’s ability to make correct predictions on new,

previously unseen data.

Consumers have more choices than ever, and they can compare prices via a wide range of channels, instantly. Dynamic pricing, also known as demand pricing, enables businesses to keep pace with accelerating market dynamics. It lets organizations flexibly price items based on factors including the level of interest of the target customer, demand at the time of purchase, and whether the customer has engaged with a marketing campaign. Acquiring new customers is more time consuming and costlier than keeping existing customers satisfied and loyal. Customer churn modeling helps organizations identify which customers are likely to stop engaging with a business—and why.

However, a model router could sometimes be a simpler,

non-machine learning algorithm. A collection of models trained independently whose predictions

are averaged or aggregated. In many cases, an ensemble produces better

predictions than a single model. For example, a

random forest is an ensemble built from multiple

decision trees. A decision forest makes a prediction by aggregating the predictions of

its decision trees. Popular types of decision forests include

random forests and gradient boosted trees.

The directory you specify for hosting subdirectories of the TensorFlow

checkpoint and events files of multiple models. A numerical metric called AUC summarizes the ROC curve into

a single floating-point value. In DQN-like algorithms, the memory used by the agent

to store state transitions for use in

experience replay. Regularization can also be defined as the penalty on a model’s complexity. For example,

a scalar has rank 0, a vector has rank 1, and a matrix has rank 2.

They control the learning process and significantly impact model performance. The next step is to select the appropriate machine learning algorithm that is suitable for our problem. This step requires knowledge of the strengths and weaknesses of different algorithms.

The main advantage of an uplift model is that it can generate predictions

for the unobserved situation (the counterfactual) and use it to compute

the causal effect. Each example in a dataset should belong to only one of the preceding subsets. For instance, a single example shouldn’t belong to both the training set and

the test set. Choosing the best temperature depends on the specific application and

the preferred properties of the model’s output.

Both processes involve using computer power to uncover hidden value in digital information. In the wake of an unfavorable event, such as South African miners going on strike, the computer algorithm adjusts its parameters automatically to create a new pattern. This way, the computational model built into the machine stays current even with changes in world events and without needing a human to tweak its code to reflect the changes. Because the asset manager received this new data on time, they are able to limit their losses by exiting the stock. ML models require continuous monitoring, maintenance, and updates to ensure they remain accurate and effective over time.

In decision analysis, a decision tree can be used to visually and explicitly represent decisions and decision making. In data mining, a decision tree describes data, but the resulting classification tree can be an input for decision-making. At its core, the method simply uses algorithms – essentially lists of rules – adjusted and refined using past data sets to make predictions and categorizations when confronted with new data.

After k rounds of training and testing, you calculate the mean and

standard deviation of the chosen test metric(s). In machine-learning

image-detection tasks, IoU is used to measure the accuracy of the model’s

predicted bounding box with respect to the

ground-truth bounding box. A machine learning approach, often used for object classification,

designed to train effective classifiers from only a small number of

training examples.

From self-driving cars to personalized recommendations on streaming platforms, ML algorithms are revolutionizing various aspects of our lives. Reinforcement learning is used to train robots to perform tasks, like walking

around a room, and software programs like

AlphaGo

to play the game of Go. ML offers a new way to solve problems, answer complex questions, and create new

content. ML can predict the weather, estimate travel times, recommend

songs, auto-complete sentences, summarize articles, and generate

never-seen-before images.

Typically such decision trees, or classification trees, output a discrete answer; however, using regression trees, the output can take continuous values (usually a real number). A text-to-text transfer learning model

introduced by

Google AI in 2020. T5 is an encoder-decoder model, based on the

Transformer architecture, trained on an extremely large

dataset. It is effective at a variety of natural language processing tasks,

such as generating text, translating languages, and answering questions in

a conversational manner. Using statistical or machine learning algorithms to determine a group’s

overall attitude—positive or negative—toward a service, product,

organization, or topic.

Interested in machine learning but you keep seeing terms unfamiliar to you? This A-to-Z glossary defines key machine learning terms you need to know. Customer lifetime value models are especially effective at predicting the future revenue that an individual customer will bring to a business in a given period. Chat GPT This information empowers organizations to focus marketing efforts on encouraging high-value customers to interact with their brand more often. Customer lifetime value models also help organizations target their acquisition spend to attract new customers that are similar to existing high-value customers.

For over 25-years he has continually featured topics in TV Tech magazine—penning the magazine’s Storage and Media Technologies and its Cloudspotter’s Journal columns. This in turn opens the door to another level of AI—that is risk, fraud protection analysis and monitoring. It’s a huge cost to the credit card companies, but one that must be spent in order to protect their integrity. Stronger forms of AI, like AGI and ASI, incorporate human behaviors more prominently, such as the ability to interpret tone and emotion. AGI would perform on par with another human, while ASI—also known as superintelligence—would surpass a human’s intelligence and ability. Neither form of Strong AI exists yet, but research in this field is ongoing.

These are just a few examples of the algorithms used in machine learning. Depending on the problem, different algorithms or combinations may be more suitable, showcasing the versatility and adaptability of ML techniques. Reinforcement learning

models make predictions by getting rewards

or penalties based on actions performed within an environment.

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