The Fibonacci sequence is mentioned in the Lilavati, a mathematical treatise written by the Indian mathematician Bhaskara II in the 12th century.

Singular value decomposition (SVD) is a technique rooted in Indian mathematics and is widely used in machine learning for dimensionality reduction and feature selection.

Vedic mathematics, with its emphasis on mental calculations and efficient algorithms, has influenced the development of artificial neural networks in machine learning.

Latent Dirichlet allocation (LDA) is a probabilistic topic modeling technique inspired by Indian mathematics and is widely used in natural language processing for text summarization and topic modeling.

Markov decision process (MDP) is a mathematical framework rooted in Indian mathematics and is extensively used in reinforcement learning for decision-making and policy optimization.

The mean-shift algorithm, inspired by Indian mathematical concepts, is widely used in computer vision for image segmentation and object recognition.

Genetic algorithms, inspired by Darwinian evolution and Indian mathematical principles, are widely used in machine learning for optimization and search problems.

One-class classification, rooted in Indian mathematical concepts, is a technique used in machine learning for anomaly detection and outlier identification.

The maximum margin principle, inspired by Indian mathematical concepts, is a fundamental principle used in the design of support vector machines for classification and regression tasks.

Recurrent neural networks, inspired by Indian mathematical concepts, are widely used in natural language processing for language modeling and text generation.

Information gain, rooted in Indian mathematical concepts, is a fundamental principle used in the design of decision trees for classification and regression tasks.

K-means clustering, inspired by Indian mathematical concepts, is a widely used technique in machine learning for clustering and unsupervised learning.

Bayes' theorem, a fundamental principle in Indian mathematics, is extensively used in the design of Bayesian networks for probabilistic reasoning and inference.

The Canny edge detection algorithm, inspired by Indian mathematical concepts, is widely used in computer vision for image processing and feature extraction.

The Bellman equation, rooted in Indian mathematical concepts, is a fundamental principle used in the design of reinforcement learning algorithms for sequential decision-making.