machine learning
scientific study of algorithms and statistical models that computer systems use to perform tasks without explicit instructions
explanation-based learning
form of machine learning
unsupervised learning
machine learning technique
meta-learning
subfield of machine learning where automatic learning algorithms are applied to metadata about machine learning experiments
softmax function
function that maps a k-element real-valued vector to a k-element categorical probability distribution
M-Theory
learning framework inspired by feed-forward processing in the ventral stream of visual cortex and originally developed for recognition and classification of objects in visual scenes
artificial neural network
computational model used in machine learning, based on connected, hierarchical functions
ensemble learning
in machine learning, the use of multiple algorithms to obtain better predictive performance than from any of the constituent learning algorithms alone
supervised learning
machine learning task of learning a function that maps an input to an output based on example input-output pairs
linear regression
statistical approach for modeling the relationship between a scalar dependent variable and one or more explanatory variables
adversarial machine learning
machine learning technique that attempts to prevent models being fooled by supplying deceptive input
multiple kernel learning
machine learning methods that use a predefined set of kernels and learn an optimal linear or non-linear combination of kernels as part of the algorithm
federated learning
method for artificial intelligence
knowledge distillation
machine learning method to transfer knowledge from a large model to a smaller one
recurrent neural network
class of artificial neural network where connections between units form a directed graph along a temporal sequence
bag-of-words model
model of text which uses a representation of text that is based on an unordered collection (a "bag") of words
automated machine learning
process of automating the end-to-end process of machine learning
machine learning in video games
use of machine learning in video games
machine learning in physics
applications of machine learning to quantum physics
feedforward neural network
artificial neural network in which connections between the nodes do not form a cycle
one-shot learning
object categorization problem that aims to learn information about object categories from one training sample
online machine learning
a method where a model is trained incrementally on data as it becomes available, in contrast to batch learning where the entire dataset is used at once
multi-task learning
form of machine learning where a model learns multiple tasks
statistical relational learning
subdiscipline of artificial intelligence
structured prediction
supervised machine learning techniques
recursive neural network
artificial neural network with connections forming a hierarchical structure, processing data recursively
quantum machine learning
Quantum Machine Learning combines quantum computing and ML to enhance algorithms, leveraging unique quantum properties, like superposition and entanglement , for efficient problem-solving.
rule-based machine learning
machine learning methods that try to develop rules that are to be applied in particular contexts
reinforcement learning
type of machine learning where an agent learns how to behave in an environment by performing actions and receiving rewards or penalties in return, aiming to maximize the cumulative reward over time
transfer learning
research problem in machine learning (ML) that focuses on storing knowledge gained while solving one problem and applying it to a different but related problem
incremental learning
method of machine learning
self-supervised learning
class of machine learning techniques in which a task is solved based on pseudo-labels which help initialize weights the weight, then the actual task is performed with supervised or unsupervised learning
pattern recognition
branch of machine learning
hierarchical temporal memory
biological theory of intelligence
nonlinear dimensionality reduction
summary of algorithms for nonlinear dimensionality reduction
zero-shot learning
problem setup in machine learning, where at test time, a learner observes samples from classes that were not observed during training, and needs to predict the class they belong to
offline machine learning
method of machine learning
preference learning
Subfield of machine learning
robot learning
machine learning for robots
feature learning
a set of techniques that learn a feature: a transformation of raw data input to a representation that can be effectively exploited in machine learning tasks
sequence-to-sequence learning
family of machine learning approaches
generative pre-trained transformer
type of large language model