Machine Learning is one of the fastest growing areas of computer science with far-reaching applications. And if you looking to make a career in this field then Understanding Machine Learning: From Theory to Algorithms, is a book that is most recommend.
I have read many of the main books on machine learning, but after reading this book, I feel that it’s a book that is pretty close to the perfect one, it covers all necessary accepts that an ML engineer must know.
In contrast to many other books, this one, as the title says, it aims at having the reader understand ML.
While reading it, you can feel the effort that the writer actually makes to people teach about the hard-core of Machine Learning and application part, and also the math behind Machine Learning in a solid fashion before delving into the various algorithms.
This book focuses to generate a basic understanding of machine learning, and the algorithmic paradigms it offers, in a principled way. Each concept of ML is divided into a block and explained with real-world examples.
Understanding Machine Learning: From Theory to Algorithms, provides a theoretical account of the fundamentals underlying machine learning and the mathematical derivations that transform these principles into practical algorithms.
Following a presentation, the book covers a wide array of central topics unaddressed by previous textbooks.
These include a discussion of the computational complexity of learning and the concepts of convexity and stability; important algorithmic paradigms including stochastic gradient descent, neural networks, and structured output learning.
This book also covers emerging theoretical concepts such as the PAC-Bayes approach and compression-based bounds.
Designed for advanced undergraduates or beginning graduates, the text makes the fundamentals and algorithms of machine learning accessible to students and non-expert readers in statistics, computer science, mathematics, and engineering.
The books start with a handy, concise and clear introduction to statistical machine learning and then consistently connects those concepts to the main ML algorithms and when they put into application.
There is a lot of mathematical rigor, which might not be needed if you are not into research. The fundamentals are covered in a better manner, each chapter is about 10 pages long.
A brief summary at the beginning of each chapter gives a clear sense of what will be accomplished in it, and attention to notation makes sure that mathematics supports understanding rather than getting in the way.
Understanding Machine Learning: From Theory to Algorithms, is definitely not a “how to” book, but rather a “what and why” book, focused on understanding principles and connections between them. I read the book cover to cover, and I was left with a sense of machine learning as a coherent discipline, and a solid feel for the main concepts.
If we talk about writing style, then this book is very well written, it goes so much into detail but it’s still very intuitive. Small chapters are very informative and keep you interested in the topics.
The only thing that I didn’t like about it is that there are 31 chapters in 360 pages, along with exercises but without answers which might go tough for beginners.
Table of Contents
Part I: Foundations
- A gentle start
- A formal learning model
- Learning via uniform convergence
- The bias-complexity trade-off
- The VC-dimension
- Non-uniform learnability
- The runtime of learning
Part II: From Theory to Algorithms
- Linear predictors
- Model selection and validation
- Convex learning problems
- Regularization and stability
- Stochastic gradient descent
- Support vector machines
- Kernel methods
- Multiclass, ranking, and complex prediction problems
- Decision trees
- Nearest neighbor
- Neural networks
Part III: Additional Learning Models
- Online learning
- Dimensionality reduction
- Generative models
- Feature selection and generation
Part IV: Advanced Theory
- Rademacher complexities
- Covering numbers
- Proof of the fundamental theorem of learning theory
- Multiclass learnability
- Compression bounds
- Technical lemmas
- Measure concentration
- Linear algebra
Source: TechGrabyteRelated posts: