Book
Understanding Machine Learning: From Theory to Algorithms
| Identifier | --shaishalev-1654 |
|---|---|
| Penulis / Pengarang | Shai Shalev-Shwartz and Shai Ben-David |
| Kata Kunci | Machine Learning Artificial intelligence |
| Tanggal Publikasi | 18 Mar 2014 |
| Bahasa | Bahasa Inggris |
| Abstrak / Deskripsi | Machine learning is one of the fastest-growing areas of computer science, with far-reaching applications. The aim of this textbook is to introduce machine learning, and the algorithmic paradigms it offers, in a principled way. The book provides an extensive theoretical account of the fundamental ideas underlying machine learning and the mathematical derivations that transform these principles into practical algorithms. Following a presentation of the basics of the ï¬Âeld, the book covers a wide array of central topics that have not been addressed 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; and emerging theoretical concepts such as the PAC-Bayes approach and compression-based bounds. Designed for an advanced undergraduate or beginning graduate course, the text makes the fundamentals and algorithms of machine learning accessible to students and non-expert readers in statistics, computer science, mathematics, and engineering. |
Attached Files / Download (1)
| No | Nama Berkas | Format | Ukuran | Aksi |
|---|---|---|---|---|
| 1 | understanding-machine-learning-theory-algorithms.pdf | application/pdf | 2,5 MB | ⬇️ Buka / Download |
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