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
Nonnegative Matrix Factorization
Tensor Decompositions: Algorithms
Tensor Decompositions: Applications
Gaussian Mixture Models
Algorithmic Aspects of Machine Learning
Open Publication License
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