Mathematical analysis of machine learning algorithms (Record no. 567580)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 02102 a2200169 4500 |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| ISBN | 9781009098380 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 006.31 |
| Item number | Z61m |
| 100 ## - MAIN ENTRY--AUTHOR NAME | |
| Personal name | Zhang, Tong |
| 245 ## - TITLE STATEMENT | |
| Title | Mathematical analysis of machine learning algorithms |
| Statement of responsibility, etc | Tong Zhang |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) | |
| Name of publisher | Cambridge University Press |
| Year of publication | 2023 |
| Place of publication | Cambridge |
| 300 ## - PHYSICAL DESCRIPTION | |
| Number of Pages | xiii, 453p |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc | The mathematical theory of machine learning not only explains the current algorithms but can also motivate principled approaches for the future. This self-contained textbook introduces students and researchers of AI to the main mathematical techniques used to analyze machine learning algorithms, with motivations and applications. Topics covered include the analysis of supervised learning algorithms in the iid setting, the analysis of neural networks (e.g. neural tangent kernel and mean-field analysis), and the analysis of machine learning algorithms in the sequential decision setting (e.g. online learning, bandit problems, and reinforcement learning). Students will learn the basic mathematical tools used in the theoretical analysis of these machine learning problems and how to apply them to the analysis of various concrete algorithms. This textbook is perfect for readers who have some background knowledge of basic machine learning methods, but want to gain sufficient technical knowledge to understand research papers in theoretical machine learning.<br/><br/>Provides a self-contained, systematic treatment of theoretical machine learning, allowing students to learn the subject in a comprehensive and systematic way<br/>Serves as a reference for many useful results normally scattered among different publications<br/>Readers learn how to apply newly learned tools and algorithms to concrete machine learning methods<br/>Focuses on the analysis of two common learning models – supervised learning and online learning – and covers all key ideas, including the recent analysis of neural networks. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical Term | Machine learning -- Mathematical models |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical Term | Algorithms (Computer science) |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical Term | Supervised learning (Machine learning) |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | Books |
| Withdrawn status | Lost status | Damaged status | Not for loan | Collection code | Home library | Current library | Date acquired | Source of acquisition | Cost, normal purchase price | Full call number | Accession Number | Cost, replacement price | Koha item type |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| On Display | PK Kelkar Library, IIT Kanpur | PK Kelkar Library, IIT Kanpur | 28/07/2025 | 2 | 3951.92 | 006.31 Z61m | A186932 | 5269.23 | Books |