Foundations of Machine Learning
Cover from Open Library
This book is part of my planned study in the theoretical underpinnings of machine learning algorithms.
Why it’s in my collection: After experimenting with practical implementations, I realized I needed stronger theoretical grounding to progress in AI research. I’ve chosen this text specifically for its focus on the mathematical guarantees and limitations of learning algorithms rather than just implementation details. I’m particularly interested in the chapters on VC dimension and generalization bounds, as understanding these theoretical foundations should help me make more informed decisions about model selection and validation approaches in my future research work.
A detailed reflection will be added after I complete this book.