Pattern Recognition and Machine Learning
by Christopher M. BishopA seminal work that integrates statistical theory with machine learning, essential for understanding algorithmic foundations.
The Elements of Statistical Learning: Data Mining, Inference, and Prediction
by Trevor Hastie, Robert Tibshirani, Jerome FriedmanA cornerstone text that provides deep insights into statistical learning principles, crucial for advanced data science applications.
Deep Learning
by Ian Goodfellow, Yoshua Bengio, Aaron CourvilleAn authoritative guide that delves into deep learning techniques, bridging theory and practice in machine learning.
Bayesian Reasoning and Machine Learning
by David BarberExplores Bayesian methods in machine learning, offering a fresh perspective on algorithm development and data interpretation.
Machine Learning: A Probabilistic Perspective
by Kevin P. MurphyA comprehensive view of machine learning through a probabilistic lens, essential for advanced algorithmic strategies.
Mathematics for Machine Learning
by Marc Peter Deisenroth, A. Aldo Faisal, Cheng Soon OngConnects mathematical concepts directly to machine learning applications, enhancing your theoretical understanding.
Reinforcement Learning: An Introduction
by Richard S. Sutton, Andrew G. BartoA foundational text on reinforcement learning, detailing algorithms and their mathematical underpinnings.
Convex Optimization
by Stephen Boyd, Lieven VandenberghePivotal for understanding optimization in machine learning, this book covers mathematical techniques crucial for algorithm efficiency.