Machine Learning (Paperback)

Paperback. This book is a research-oriented roadmap through modern ML, structured via three lenses: Research Perspectives, Recent Advances, and Future Directions. Part I establishes rigorous methodologies, data-centric AI (active learning, weak supervision, bias mitigation), and critical evaluation beyond single metrics. Part II surveys neural architectures (Transformers to State-Space models), optimization (SAM, double descent), generative modeling (diffusion unification), geometric deep learning, and self-supervised/multi-modal learning. Part III covers foundation models-scaling laws, emergent abilities (in-context learning, chain-of-thought), generative AI frontiers (hallucination, controllable generation), and neurosymbolic approaches (RAG, tool use). Part IV addresses grand challenges: efficient/sustainable ML, continual/meta-learning, interpretability/robustness/alignment (RLHF, causal representation), and pathways to AGI with societal impact. Each chapter includes open questions and practical research checklists, aiming to move readers from consumers to contributors. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…