An Introduction to Optimization with Applications in Machine Learning and Data Analytics 1st Edition

Capture the cutting edge of mathematical problem-solving with An Introduction to Optimization with Applications in Machine Learning and Data Analytics, 1st Edition by Jeffrey Paul Wheeler. This accessible, rigorous text draws you into the mechanics behind modern algorithms and shows how optimization drives real-world insights across industries.

Begin with clear, motivating examples that reveal why optimization matters for predictive modeling, feature selection, and large-scale data problems. The book builds intuition and technique in a stepwise way—covering core concepts such as convex and nonconvex optimization, gradient-based methods, stochastic algorithms, regularization, and constrained optimization—so readers quickly connect theory to practice.

Whether you’re a graduate student, researcher, or data professional, you’ll appreciate the balanced blend of mathematical foundations and applied perspectives. Chapters emphasize algorithmic thinking, performance trade-offs, and how optimization choices affect model accuracy and scalability in machine learning and data analytics workflows. Realistic examples and problem-focused explanations make complex topics approachable while preparing you to implement and evaluate optimization strategies in projects and research.

Perfect for readers in the US, UK, Europe, India, and beyond, this edition serves as both a classroom companion and a practical reference for industry work. If you want to deepen your understanding of how optimization shapes modern data science and gain the skills to design robust algorithms, this book is an essential addition to your library.

Add An Introduction to Optimization with Applications in Machine Learning and Data Analytics to your collection today and start transforming theory into impactful, data-driven solutions.

Note: eBooks do not include supplementary materials such as CDs, access codes, etc.