ISBN: 9781107184589 - algorithmic aspects of machine learning (13 Ergebnisse)

Zustand: good. This is a pre-loved book that shows moderate signs of wear from previous reading. You may notice creases, edge wear, or a cracked spine, but it remains in solid, readable condition. Please note: -May include library or rental stickers, stamps, or markings. -Supplemental materials e.g., CDs, access codes, inserts are not guaranteed. -Box sets may not come with the original outer box. If it does, the box will not be in perfect condition. Your satisfaction is our top priority! If you have any questions or concerns about your order, please don't hesitate to reach out. Thank you for shopping with us and supporting small businessâ"happy reading.…

Zustand: New.

Zustand: New. In English.

Zustand: New.

Hardcover. Zustand: Brand New. 151 pages. 9.25x6.25x0.75 inches. In Stock.

Zustand: New. 2018. 1st Edition. Hardcover. . . . . . Books ship from the US and Ireland.

Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book bridges theoretical computer science and machine learning by exploring what the two sides can teach each other. It emphasizes the need for flexible, tractable models that better capture not what makes machine learning hard, but what makes it easy. Theoretical computer scientists will be introduced to important models in machine learning and to the main questions within the field. Machine learning researchers will be introduced to cutting-edge research in an accessible format, and gain familiarity with a modern, algorithmic toolkit, including the method of moments, tensor decompositions and convex programming relaxations. The treatment beyond worst-case analysis is to build a rigorous understanding about the approaches used in practice and to facilitate the discovery of exciting, new ways to solve important long-standing problems.…

Zustand: New. 2018. 1st Edition. Hardcover. . . . . .

Hardback. Zustand: New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days.

Zustand: New. Print on Demand.

Hardcover. Zustand: new. Hardcover. This book bridges theoretical computer science and machine learning by exploring what the two sides can teach each other. It emphasizes the need for flexible, tractable models that better capture not what makes machine learning hard, but what makes it easy. Theoretical computer scientists will be introduced to important models in machine learning and to the main questions within the field. Machine learning researchers will be introduced to cutting-edge research in an accessible format, and gain familiarity with a modern, algorithmic toolkit, including the method of moments, tensor decompositions and convex programming relaxations. The treatment beyond worst-case analysis is to build a rigorous understanding about the approaches used in practice and to facilitate the discovery of exciting, new ways to solve important long-standing problems. Machine learning is reshaping our everyday life. This book explores the theoretical underpinnings in an accessible way, offering theoretical computer scientists an introduction to important models and problems and offering machine learning researchers a cutting-edge algorithmic toolkit. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. …

Zustand: New. PRINT ON DEMAND.

Zustand: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Machine learning is reshaping our everyday life. This book explores the theoretical underpinnings in an accessible way, offering theoretical computer scientists an introduction to important models and problems and offering machine learning researchers a cut.…