**by Ankur Moitra**

DescriptionTable of ContentsDetailsHashtagsReport an issue ### Book Description

This course is organized around algorithmic issues that arise in machine learning. Modern machine learning systems are often built on top of algorithms that do not have provable guarantees, and it is the subject of debate when and why they work. In this class, we focus on designing algorithms whose performance we can rigorously analyze for fundamental machine learning problems.

The book is based on the class "Algorithmic Aspects of Machine Learning" taught at MIT in Fall 2013, Spring 2015 and Fall 2017. ### Table of Contents

### Book Details

### Related Books

The book is based on the class "Algorithmic Aspects of Machine Learning" taught at MIT in Fall 2013, Spring 2015 and Fall 2017.

This open book is licensed under a Open Publication License (OPL). You can download Algorithmic Aspects of Machine Learning ebook for free in PDF format (1.5 MB).

Chapter 1

Introduction

Chapter 2

Nonnegative Matrix Factorization

Chapter 3

Tensor Decompositions: Algorithms

Chapter 4

Tensor Decompositions: Applications

Chapter 5

Sparse Recovery

Chapter 6

Sparse Coding

Chapter 7

Gaussian Mixture Models

Chapter 8

Matrix Completion

Title

Algorithmic Aspects of Machine Learning

Subject

Computer Science

Publisher

Self-publishing

Published

2018

Pages

249

Edition

1

Language

English

PDF Size

1.5 MB

License

Open Publication License

The world of machine learning is evolving so quickly that it's challenging to find real-life use cases that are relevant to your day-to-day work.
That's why we've created this comprehensive guide you can start using right away. Get everything you need - use cases, code samples and notebooks - so you can start putting the Databrick...

A new edition of a graduate-level machine learning textbook that focuses on the analysis and theory of algorithms.
This book is a general introduction to machine learning that can serve as a textbook for graduate students and a reference for researchers. It covers fundamental modern topics in machine learning while providing the theoretical basi...

AI is transforming numerous industries. Machine Learning Yearning, a free ebook from Andrew Ng, teaches you how to structure Machine Learning projects.
This book is focused not on teaching you ML algorithms, but on how to make ML algorithms work. After reading Machine Learning Yearning, you will be able to:
- Prioritize the most promising direc...

This book proceedings presents new approaches to Machine Learning for Cyber Physical Systems, experiences and visions. It contains some selected papers from the international Conference ML4CPS - Machine Learning for Cyber Physical Systems, which was held in Karlsruhe, October 23-24, 2018. Cyber Physical Systems are characterized by their ability to...

This book presents the first comprehensive overview of general methods in Automated Machine Learning (AutoML), collects descriptions of existing systems based on these methods, and discusses the first series of international challenges of AutoML systems. The recent success of commercial ML applications and the rapid growth of the field has created ...

This textbook presents fundamental machine learning concepts in an easy to understand manner by providing practical advice, using straightforward examples, and offering engaging discussions of relevant applications. The main topics include Bayesian classifiers, nearest-neighbor classifiers, linear and polynomial classifiers, decision trees, neural ...