The Mathematics of Machine Learning: Lectures on Supervised Methods and Beyond by Maria Han Veiga, François Gaston Ged
Requirements: .ePUB, .PDF reader, 29.0 MB
Overview: This book is an introduction to Machine Learning, with a strong focus on the mathematics behind the standard algorithms and techniques in the field, aimed at senior undergraduates and early graduate students of Mathematics. There is a focus on well-known Supervised Machine Learning algorithms, detailing the existing theory to provide some theoretical guarantees, featuring intuitive proofs and exposition of the material in a concise and precise manner. A broad set of topics is covered, giving an overview of the field. A summary of the topics covered is: statistical learning theory, approximation theory, linear models, kernel methods, Gaussian processes, deep neural networks, ensemble methods and unsupervised learning techniques, such as clustering and dimensionality reduction. Machine Learning aims at building algorithms that autonomously learn how to perform a task from examples. This definition is rather vague on purpose, but to make it slightly clearer, by “autonomously” we mean that no expert is teaching (or coding by hand) the solution; by “learn” we mean that we have a measure of performance of the algorithm output on the task. This book is suited for students who are interested in entering the field, by preparing them to master the standard tools in Machine Learning. The reader will be equipped to understand the main theoretical questions of the current research and to engage with the field.
Genre: Non-Fiction > Tech & Devices
Download Instructions:
https://tbit.to/qc71ig8l6brb.html
https://katfile.com/enzhc2js0hsp/The_Mathematics_of_Machine_Learning.rar.html
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Requirements: .ePUB, .PDF reader, 29.0 MB
Overview: This book is an introduction to Machine Learning, with a strong focus on the mathematics behind the standard algorithms and techniques in the field, aimed at senior undergraduates and early graduate students of Mathematics. There is a focus on well-known Supervised Machine Learning algorithms, detailing the existing theory to provide some theoretical guarantees, featuring intuitive proofs and exposition of the material in a concise and precise manner. A broad set of topics is covered, giving an overview of the field. A summary of the topics covered is: statistical learning theory, approximation theory, linear models, kernel methods, Gaussian processes, deep neural networks, ensemble methods and unsupervised learning techniques, such as clustering and dimensionality reduction. Machine Learning aims at building algorithms that autonomously learn how to perform a task from examples. This definition is rather vague on purpose, but to make it slightly clearer, by “autonomously” we mean that no expert is teaching (or coding by hand) the solution; by “learn” we mean that we have a measure of performance of the algorithm output on the task. This book is suited for students who are interested in entering the field, by preparing them to master the standard tools in Machine Learning. The reader will be equipped to understand the main theoretical questions of the current research and to engage with the field.
Genre: Non-Fiction > Tech & Devices
Download Instructions:
https://tbit.to/qc71ig8l6brb.html
https://katfile.com/enzhc2js0hsp/The_Mathematics_of_Machine_Learning.rar.html
Trouble downloading? Read This.