Deep Learning (adaptive Computation And Machine Learning Series)

Principles of Data Mining (Adaptive Computation and Machine Learning) by David J. Hand[Repost]

Principles of Data Mining (Adaptive Computation and Machine Learning) (Scan) by David J. Hand
English | Aug 1, 2001 | ISBN: 026208290X | 546 Pages | PDF | 30 MB

The growing interest in data mining is motivated by a common problem across disciplines: how does one store, access, model, and ultimately describe and understand very large data sets? Historically, different aspects of data mining have been addressed independently by different disciplines.
David J. Hand, Principles of Data Mining (Adaptive Computation and Machine Learning) (Repost)

David J. Hand, Principles of Data Mining (Adaptive Computation and Machine Learning)
ISBN: 026208290X | edition 2001 | PDF | 578 pages | 30 mb

The growing interest in data mining is motivated by a common problem across disciplines: how does one store, access, model, and ultimately describe and understand very large data sets? Historically, different aspects of data mining have been addressed independently by different disciplines. This is the first truly interdisciplinary text on data mining, blending the contributions of information science, computer science, and statistics. The book consists of three sections.
Learning Kernel Classifiers: Theory and Algorithms  (Adaptive Computation and Machine Learning)

Ralf Herbrich, "Learning Kernel Classifiers: Theory and Algorithms"
The M.I.T Press | 2001 | ISBN: 026208306X | 384 pages | PDF | 2,4 MB
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond

Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond (Adaptive Computation and Machine Learning) by Bernhard Schlkopf, Alexander J. Smola
Publisher: The MIT Press; 1st edition (December 15, 2001) | ISBN-10: 0262194759 | PDF | 36,2 Mb | 644 pages

In the 1990s, a new type of learning algorithm was developed, based on results from statistical learning theory: the Support Vector Machine (SVM). This gave rise to a new class of theoretically elegant learning machines that use a central concept of SVMs— -kernels–for a number of learning tasks. Kernel machines provide a modular framework that can be adapted to different tasks and domains by the choice of the kernel function and the base algorithm. They are replacing neural networks in a variety of fields, including engineering, information retrieval, and bioinformatics. Learning with Kernels provides an introduction to SVMs and related kernel methods. Although the book begins with the basics, it also includes the latest research. It provides all of the concepts necessary to enable a reader equipped with some basic mathematical knowledge to enter the world of machine learning using theoretically well-founded yet easy-to-use kernel algorithms and to understand and apply the powerful algorithms that have been developed over the last few years.
Principles of Data Mining (Adaptive Computation and Machine Learning) by  David J. Hand

Principles of Data Mining (Adaptive Computation and Machine Learning) by David J. Hand, Heikki Mannila, Padhraic Smyth
Publisher: The MIT Press (August 1, 2001) | ISBN-10: 026208290X | PDF | 30,6 Mb | 425 pages

The growing interest in data mining is motivated by a common problem across disciplines: how does one store, access, model, and ultimately describe and understand very large data sets? Historically, different aspects of data mining have been addressed independently by different disciplines. This is the first truly interdisciplinary text on data mining, blending the contributions of information science, computer science, and statistics. The book consists of three sections. The first, foundations, provides a tutorial overview of the principles underlying data mining algorithms and their application. The presentation emphasizes intuition rather than rigor. The second section, data mining algorithms, shows how algorithms are constructed to solve specific problems in a principled manner.

Learning Path: Intermediate Data Science with R  eBooks & eLearning

Posted by FenixN at Dec. 5, 2016
Learning Path: Intermediate Data Science with R

Learning Path: Intermediate Data Science with R
HDRips | MP4/AVC, ~757 kb/s | 1920х1080 / 1280x720 | Duration: 13:00:13 | English: AAC, 128 kb/s (2 ch)
Size: 4,78 GB | Genre: Development / Programming

The R programming language has arguably become the single most important tool for computational statistics, visualization, and data science. With this Learning Path, master all the features you'll need as a data scientist, from the basics to more advanced techniques including R Graph and machine learning. You'll work your data like never before.

Learning From Data - Introductory Machine Learning Course  eBooks & eLearning

Posted by FenixN at Nov. 28, 2016
Learning From Data - Introductory Machine Learning Course

Learning From Data - Introductory Machine Learning Course
HDRips | MP4/AVC, ~222 kb/s | 1280x720 | Duration: 23:36:03 | English: AAC, 96 kb/s (2 ch) | 3.22 GB
Genre: Science

This introductory computer science course in machine learning will cover basic theory, algorithms, and applications. Machine learning is a key technology in Big Data, and in many financial, medical, commercial, and scientific applications. It enables computational systems to automatically learn how to perform a desired task based on information extracted from the data. Machine learning has become one of the hottest fields of study today and the demand for jobs is only expected to increase. Gaining skills in this field will get you one step closer to becoming a data scientist or quantitative analyst.
Human Computation (Synthesis Lectures on Artificial Intelligence and Machine Learning) (repost)

Edith Law, Luis von Ahn, "Human Computation (Synthesis Lectures on Artificial Intelligence and Machine Learning)"
Mor gan & Clay pool Publis hers | 2011 | ISBN: 1608455165 | 121 pages | PDF | 3 MB
Visual Saliency Computation: A Machine Learning Perspective (repost)

Visual Saliency Computation: A Machine Learning Perspective by Jia Li and Wen Gao
English | 2014 | ISBN-10: 3319056417 | 254 pages | PDF | 32,5 MB

This book covers fundamental principles and computational approaches relevant to visual saliency computation. As an interdisciplinary problem, visual saliency computation is introduced in this book from an innovative perspective that combines both neurobiology and machine learning.
Visual Saliency Computation: A Machine Learning Perspective

Jia Li, Wen Gao, "Visual Saliency Computation: A Machine Learning Perspective"
2014 | ISBN-10: 3319056417 | 254 pages | PDF | 33 MB