Key Machine Learning Definitions
By: Roberto Iriondo
Tags: Machine Learning, Artificial Intelligence, Machine Learning Modeling, Machine Learning Models, Machine Learning Terms

Glossary of Terms | Sorted (A-Z)

AI Agent:

An AI agent or intelligent agent is a bot used in AI-related tasks.

Algorithm:

An algorithm is a process that follows a set of rules, a problem solver — mainly used by computers.

AlphaGo:

AlphaGo is a computer program that plays the board game Go. It is recognized as the first Computer Go program to beat a professional human Go player [5].

Artificial Intelligence (AI):

Artificial intelligence (AI), as defined by Professor Andrew Moore, is the science and engineering of making computers behave in ways that, until recently, we thought required human intelligence [1].

Autonomous:

Being autonomous refers to a device or tool capable of operating without direct human control.

Backpropagation:

Backpropagation is the primary algorithm for gradient descent, in which the output calculates the values of each node in a forward-pass neural network. Then, the partial derivative of the error respective to each parameter calculates in a backward pass neural network through the graph.

Black box:

A black box is a complex neural network in which algorithms, contents, and decision-making processes are unknown to the end-user.

Bot:

A bot is an autonomous program that can interact with computer systems, programs, or users. Mostly, directly or indirectly supervised by a human.

Clustering:

Clustering directly refers to unsupervised machine learning.

Computational learning theory:

Computational learning theory is the sub-field of AI, which is devoted to studying the design and analysis of machine learning algorithms [4].

Computer program:

A computer program is a collection of instructions that perform specific tasks when launched by a computer.

Computer science (CS):

Computer Science is the scientific study of the principles and use of computers.

Computer vision:

Computer vision is an interdisciplinary scientific sub-field of AI and computer science that aims at giving computers a visual understanding of its input.

Convolutional neural network (CNN):

A convolutional neural network (CNN) is a class of deep neural network (DNN) used on image recognition, processing, and analysis — specifically designed to process pixel data.

Data:

Data is a digital collection of information.

Data Cleansing:

Data cleansing or data cleaning refers to the quality assurance process of datasets. The datasets are scrutinized to find and correct erroneous data records in the database(s).

Data mining:

Data mining is the process of examination and discovery of patterns in data to generate new information.

Datasets:

A dataset is a collection of related sets of data that is composed of separate elements but can be manipulated as a unit by a computer.

Data science:

Data science is an interdisciplinary scientific field, which focuses on the processes and systems to extract knowledge or insights from data in its various forms.

Deep learning:

Deep learning is a subset of machine learning in which layered neural networks, combined with high computing power and large datasets, can create powerful machine learning models.

Deep neural network (DNN):

A deep neural network (DNN) is a large computer system modeled after the human brain.

Dimensional reduction:

Dimensional reduction refers to the process of reducing the number of random variables by obtaining a set of principal variables via feature selection and/or feature extraction.

Explainable AI:

An explainable AI is an AI system capable of explaining its decision-making process.

Generative adversarial networks (GANs):

A generative adversarial network (GAN) are two neural networks contesting with each other in a zero-sum game framework.

Heuristics:

Heuristics refers to when rules are drawn from experience used to solve a problem faster than other traditional problem-solving methods in AI. While faster, a heuristic approach typically is less optimal than the classic methods it replaces.

Input:

Input is what is put in, taken in, or operated via a process or computer system.

Intelligence:

Intelligence is the computational part of the ability to achieve goals in the world. Varying kinds and degrees of intelligence occur in people, many animals, and some machines.

Linear Algebra:

Linear algebra is a branch of mathematics concerning vector spaces and linear mappings between such spaces. It includes the study of lines, planes, and sub-spaces, yet is also concerned with properties common to all vector spaces.

Long short-term memory networks (LSTMs):

Long short-term memory networks (LSTMs) are unique kinds of recurrent neural networks capable of learning long-term dependencies [2].

Machine learning (ML):

As defined by Professor Tom Mitchell, machine learning refers to a scientific branch of AI, which focuses on the study of computer algorithms that allow computer programs to automatically improve through experience [3].

Machine learning model:

Machine learning modeling refers to a question/answering system fed with cleansed data, that takes care of processing ML related tasks. The machine learning model can be referred to as a complex representation of a process.

Machine perception:

Machine perception is the capability of a computer system to interpret data like the way humans use their senses to interact and relate to the world around them.

Natural Language Processing (NLP):

Natural language processing (NLP) is a scientific branch of AI, which focuses on helping computers understand, interpret, and manipulate human language.

Neural network:

A neural network is a computer system modeled after the human brain.

Recurrent neural network (RNN):

A recurrent neural network (RNN) is a powerful and robust type of neural network, capable of complex cycles in internal memory.

Turing test:

The famous Turing test refers to a test that only passes if a human being is unable to distinguish a machine from a human [6].

DISCLAIMER: The views expressed in this article are those of the author(s) and do not represent the views of Carnegie Mellon University, nor other companies (directly or indirectly) associated with the author(s). These writings do not intend to be final products, yet rather a reflection of current thinking, along with being a catalyst for discussion and improvement.