---
product_id: 191134821
title: "Linear Algebra and Optimization for Machine Learning: A Textbook"
price: "₩125571"
currency: KRW
in_stock: true
reviews_count: 13
url: https://www.desertcart.kr/products/191134821-linear-algebra-and-optimization-for-machine-learning-a-textbook
store_origin: KR
region: South Korea
---

# Linear Algebra and Optimization for Machine Learning: A Textbook

**Price:** ₩125571
**Availability:** ✅ In Stock

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- **What is this?** Linear Algebra and Optimization for Machine Learning: A Textbook
- **How much does it cost?** ₩125571 with free shipping
- **Is it available?** Yes, in stock and ready to ship
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## Description

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Review: Excellent and Practical Text for Machine Learning, incl. for PCA / SVD - Absolutely one of my top 3 technical book purchases for mathematics in machine learning and data science, out of my approximately 20 books total on these topics. Aggarwal has constructed a text that is both mathematically clear and with a clearly-written progression to more advanced topics in Linear Algebra. I especially purchased this book for more mathematical rigor *and* applications focus in Eigenvectors, Eigendecomposition, Principal Component Analysis, and Singular Value Decomposition; and Aggarwal has built an admirable construction covering these topics. Reference my photos of the start of the “Applications of Singular Value Decomposition” sections. (This section occurs about the 2/3 of the way through the book; so another 1/3 of the book’s content follows.) Highly recommended.
Review: Fine quality book - A very fine quality book, looks and smells great 😁 It is affordable as well. Subject review: Just started with the subject. Whatever little pickings I did, felt impressive. I plan to come back and do a thorough review after burning a few weeks/months of midnight oil!

## Technical Specifications

| Specification | Value |
|---------------|-------|
| Best Sellers Rank | #1,165,658 in Books ( See Top 100 in Books ) #61 in Linear Algebra (Books) #787 in Artificial Intelligence & Semantics #1,243 in Probability & Statistics (Books) |
| Customer Reviews | 4.5 4.5 out of 5 stars (135) |
| Dimensions  | 7.01 x 1.13 x 10 inches |
| Edition  | 1st ed. 2020 |
| ISBN-10  | 3030403432 |
| ISBN-13  | 978-3030403430 |
| Item Weight  | 2.3 pounds |
| Language  | English |
| Print length  | 516 pages |
| Publication date  | May 13, 2020 |
| Publisher  | Springer |

## Images

![Linear Algebra and Optimization for Machine Learning: A Textbook - Image 1](https://m.media-amazon.com/images/I/71wcS2WDtPL.jpg)

## Customer Reviews

### ⭐⭐⭐⭐⭐ Excellent and Practical Text for Machine Learning, incl. for PCA / SVD
*by B***R on August 8, 2020*

Absolutely one of my top 3 technical book purchases for mathematics in machine learning and data science, out of my approximately 20 books total on these topics. Aggarwal has constructed a text that is both mathematically clear and with a clearly-written progression to more advanced topics in Linear Algebra. I especially purchased this book for more mathematical rigor *and* applications focus in Eigenvectors, Eigendecomposition, Principal Component Analysis, and Singular Value Decomposition; and Aggarwal has built an admirable construction covering these topics. Reference my photos of the start of the “Applications of Singular Value Decomposition” sections. (This section occurs about the 2/3 of the way through the book; so another 1/3 of the book’s content follows.) Highly recommended.

### ⭐⭐⭐⭐⭐ Fine quality book
*by A***1 on August 7, 2025*

A very fine quality book, looks and smells great 😁 It is affordable as well. Subject review: Just started with the subject. Whatever little pickings I did, felt impressive. I plan to come back and do a thorough review after burning a few weeks/months of midnight oil!

### ⭐⭐⭐ Hard To Follow
*by M***L on April 13, 2024*

This book is very hard for me to understand. I need to read it very slowly and keep reading the same sections multiple times before it makes sense. There are very few worked out examples in the book. There are no answers to any practice exercises in the book. There are very few diagrams, and I feel like it would greatly help in explaining some concepts. Often times the author states a conclusion or consequence of some lemma, but I have no idea how that conclusion follows from the equation or lemma. Most of the practice problems and exercises are very abstract in nature, and call for proving something. For example, if a matrix has quality A, then prove that it also has quality B. These problems don't really help me to understand the material. I was expecting more practical problems like: invert this matrix, decompose this matrix, find the orthonormal basis of this matrix, find the determinant of this matrix, etc. Those sort of problems are rare, and often times I feel like the exercises don't align with the chapter's content. Problems with actual numbers could really help me to better understand the material. On the plus side, it seems to cover much more material than a standard textbook does. It could easily have been split into two or even three distinct books covering the same content. Unfortunately that means information is crammed into this and hence tough to comprehend.

## Frequently Bought Together

- Linear Algebra and Optimization for Machine Learning: A Textbook
- Mathematics for Machine Learning
- Deep Learning (Adaptive Computation and Machine Learning series)

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*Product available on Desertcart South Korea*
*Store origin: KR*
*Last updated: 2026-02-21*