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Algorithmic Aspects of Machine Learning

by Ankur Moitra

Algorithmic Aspects of Machine Learning

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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.

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).

Table of Contents

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

Book Details

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

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