Algorithmic Aspects of Machine Learning / by Ankur Moitra. [Electronic Resource]
Material type: Computer filePublication details: Cambridge : Cambridge University Press, 2018Description: viii, 152pISBN:- 9781316882177
- 006.310 151 81 M729A
Item type | Home library | Collection | Call number | Status | Notes | Date due | Barcode | Item holds |
---|---|---|---|---|---|---|---|---|
e-Book | S. R. Ranganathan Learning Hub Online | Textbook | 006.310 151 81 M729A (Browse shelf(Opens below)) | Available | Platform : Cambridge Core | EB0385 |
Browsing S. R. Ranganathan Learning Hub shelves, Shelving location: Online, Collection: Textbook Close shelf browser (Hides shelf browser)
006.3 R911A Artificial Intelligence : A Modern Approach. | 006.3 Xi4P Probabilistic Reasoning in Multiagent Systems : A Graphical Models Approach | 006.310 151 252 K962K Kernel Methods and Machine Learning | 006.310 151 81 M729A Algorithmic Aspects of Machine Learning | 006.31 Al74M Introduction to Machine Learning | 006.31 B398S Scaling Up Machine Learning : Parallel and Distributed Approaches | 006.31 J271E Evaluating Learning Algorithms : A Classification Perspective |
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.
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