|
Books > Computing & IT > Computer communications & networking
|
Buy Now
Linear Algebra and Optimization for Machine Learning - A Textbook (Hardcover, 1st ed. 2020)
Loot Price: R2,154
Discovery Miles 21 540
|
|
|
Linear Algebra and Optimization for Machine Learning - A Textbook (Hardcover, 1st ed. 2020)
Expected to ship within 10 - 15 working days
|
This textbook introduces linear algebra and optimization in the
context of machine learning. Examples and exercises are provided
throughout the book. A solution manual for the exercises at the end
of each chapter is available to teaching instructors. This textbook
targets graduate level students and professors in computer science,
mathematics and data science. Advanced undergraduate students can
also use this textbook. The chapters for this textbook are
organized as follows: 1. Linear algebra and its applications: The
chapters focus on the basics of linear algebra together with their
common applications to singular value decomposition, matrix
factorization, similarity matrices (kernel methods), and graph
analysis. Numerous machine learning applications have been used as
examples, such as spectral clustering, kernel-based classification,
and outlier detection. The tight integration of linear algebra
methods with examples from machine learning differentiates this
book from generic volumes on linear algebra. The focus is clearly
on the most relevant aspects of linear algebra for machine learning
and to teach readers how to apply these concepts. 2. Optimization
and its applications: Much of machine learning is posed as an
optimization problem in which we try to maximize the accuracy of
regression and classification models. The "parent problem" of
optimization-centric machine learning is least-squares regression.
Interestingly, this problem arises in both linear algebra and
optimization, and is one of the key connecting problems of the two
fields. Least-squares regression is also the starting point for
support vector machines, logistic regression, and recommender
systems. Furthermore, the methods for dimensionality reduction and
matrix factorization also require the development of optimization
methods. A general view of optimization in computational graphs is
discussed together with its applications to back propagation in
neural networks. A frequent challenge faced by beginners in machine
learning is the extensive background required in linear algebra and
optimization. One problem is that the existing linear algebra and
optimization courses are not specific to machine learning;
therefore, one would typically have to complete more course
material than is necessary to pick up machine learning.
Furthermore, certain types of ideas and tricks from optimization
and linear algebra recur more frequently in machine learning than
other application-centric settings. Therefore, there is significant
value in developing a view of linear algebra and optimization that
is better suited to the specific perspective of machine learning.
General
Is the information for this product incomplete, wrong or inappropriate?
Let us know about it.
Does this product have an incorrect or missing image?
Send us a new image.
Is this product missing categories?
Add more categories.
Review This Product
No reviews yet - be the first to create one!
|
You might also like..
|
Email address subscribed successfully.
A activation email has been sent to you.
Please click the link in that email to activate your subscription.
Welcome to Loot.co.za!
Sign in / Register |Wishlists & Gift Vouchers |Help | Advanced search
