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A Matrix Algebra Approach to Artificial Intelligence

Abstract

Machine learning is a subset of artificial intelligence. This chapter presents first a machine learning tree, and then focuses on the matrix algebra methods in machine learning including single-objective optimization, feature selection, principal component analysis, and canonical correlation analysis together with supervised, unsupervised, and semi-supervised learning and active learning. More importantly, this chapter highlights selected topics and advances in machine learning: graph machine learning, reinforcement learning, Q-learning, and transfer learning.

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    Xian-Da Zhang

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Zhang, XD. (2020). Machine Learning. In: A Matrix Algebra Approach to Artificial Intelligence. Springer, Singapore. https://doi.org/10.1007/978-981-15-2770-8_6

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