Название: Data-Driven Decisions: An Introduction to Machine LearningАвтор: Nandini K
Издательство: HKBK College of Engineering
Год: Nov 26, 2024
Страниц: 230
Язык: английский
Формат: pdf, epub (true)
Размер: 10.2 MB
Data-Driven Decisions: An Introduction to Machine Learning provides a comprehensive and accessible introduction to the principles and applications of Machine Learning for students, professionals, and decision-makers. Combining theoretical foundations with practical examples, this book guides readers through key concepts such as supervised and unsupervised learning, feature engineering, model evaluation, and interpretability. With a focus on how Machine Learning drives informed, data-driven decision-making across industries, the text balances technical depth with clarity. Through case studies, hands-on exercises, and discussions on ethical considerations, this book equips readers with the tools to apply Machine Learning effectively in solving real-world problems.
Machine Learning is programming computers to optimize a performance criterion using example data or past experience. We have a model defined up to some parameters, and learning is the execution of a computer program to optimize the parameters of the model using the training data or past experience. The model may be predictive to make predictions in the future, or descriptive to gain knowledge from data, or both.
Genetic algorithms (GAS) provide a learning method motivated by an analogy to biological evolution. Rather than search from general-to-specific hypotheses, or from simple-to-complex, GAS generate successor hypotheses by repeatedly mutating and recombining parts of the best currently known hypotheses. At each step, a collection of hypotheses called the current population is updated by replacing some fraction of the population by offspring of the most fit current hypotheses. The process forms a generate-and-test beam-search of hypotheses, in which variants of the best current hypotheses are most likely to be considered next. The popularity of GAS is motivated by a number of factors including:
- Evolution is known to be a successful, robust method for adaptation within biological systems.
- GAS can search spaces of hypotheses containing complex interacting parts, where the impact of each part on overall hypothesis fitness may be difficult to model.
- Genetic algorithms are easily parallelized and can take advantage of the decreasing costs of powerful computer hardware.
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