Machine Learning Refined
Foundations, Algorithms, and Applications
$78.99 (P)
- Authors:
- Jeremy Watt, Northwestern University, Illinois
- Reza Borhani, Northwestern University, Illinois
- Aggelos K. Katsaggelos, Northwestern University, Illinois
- Date Published: November 2016
- availability: Available
- format: Hardback
- isbn: 9781107123526
$
78.99
(P)
Hardback
-
Providing a unique approach to machine learning, this text contains fresh and intuitive, yet rigorous, descriptions of all fundamental concepts necessary to conduct research, build products, tinker, and play. By prioritizing geometric intuition, algorithmic thinking, and practical real world applications in disciplines including computer vision, natural language processing, economics, neuroscience, recommender systems, physics, and biology, this text provides readers with both a lucid understanding of foundational material as well as the practical tools needed to solve real-world problems. With in-depth Python and MATLAB/OCTAVE-based computational exercises and a complete treatment of cutting edge numerical optimization techniques, this is an essential resource for students and an ideal reference for researchers and practitioners working in machine learning, computer science, electrical engineering, signal processing, and numerical optimization.
Read more- Provides MATLAB-based coding exercises, real-world examples, and practical applications
- Takes a unique approach, enabling a more coherent, intuitive, and interactive way of learning
- Includes over 150 illustrations, many of which are in full colour
Customer reviews
Not yet reviewed
Be the first to review
Review was not posted due to profanity
×Product details
- Date Published: November 2016
- format: Hardback
- isbn: 9781107123526
- length: 298 pages
- dimensions: 249 x 178 x 18 mm
- weight: 0.74kg
- contains: 135 colour illus. 3 tables 81 exercises
- availability: Available
Table of Contents
1. Introduction
Part I. The Basics:
2. Fundamentals of numerical optimization
3. Knowledge-driven regression
4. Knowledge-driven classification
Part II. Automatic Feature Design:
5. Automatic feature design for regression
6. Automatic feature design for classification
7. Kernels, backpropagation, and regularized cross-validation
Part III. Tools for Large Scale Data:
8. Advanced gradient schemes
9. Dimension reduction techniques
Part IV. Appendices.-
Resources for
Machine Learning Refined
Jeremy Watt, Reza Borhani, Aggelos K. Katsaggelos
General Resources
- Author's website - sample chapters & notes
- Author's website - supplementary code & interactive demonstrations
Instructor Resources
Find resources associated with this title
Type Name Unlocked * Format Size Showing of
This title is supported by one or more locked resources. Access to locked resources is granted exclusively by Cambridge University Press to instructors whose faculty status has been verified. To gain access to locked resources, instructors should sign in to or register for a Cambridge user account.
Please use locked resources responsibly and exercise your professional discretion when choosing how you share these materials with your students. Other instructors may wish to use locked resources for assessment purposes and their usefulness is undermined when the source files (for example, solution manuals or test banks) are shared online or via social networks.
Supplementary resources are subject to copyright. Instructors are permitted to view, print or download these resources for use in their teaching, but may not change them or use them for commercial gain.
If you are having problems accessing these resources please contact lecturers@cambridge.org.
Related Books
related journals
Sorry, this resource is locked
Please register or sign in to request access. If you are having problems accessing these resources please email lecturers@cambridge.org
Register Sign in» Proceed