The Elements of Statistical Learning: Data Mining,...

Main
Mathematics - Mathematical Statistics
The Elements of Statistical Learning:...

The Elements of Statistical Learning: Data Mining, Inference, and Prediction (2nd edition) (12print 2017)

Trevor Hastie, Robert Tibshirani, Jerome Friedman

5.0 / 5.0

How much do you like this book?

What’s the quality of the file?

Download the book for quality assessment

What’s the quality of the downloaded files?

Categories:

Mathematics - Mathematical Statistics

Year:

2017

Edition:

2

Language:

english

File:

PDF, 12.93 MB

IPFS:

,

english, 2017

Read Online

Conversion to is in progress

Conversion to is failed

You may be interested in

Lijun Wang — 统计学习基础 The Elements of Statistical Learning

Trevor Hastie, Robert Tibshirani, Jerome Friedman — The Elements of Statistical Learning: Data Mining, Inference, and Prediction.

Roger D. Peng, Elizabeth Matsui — The Art of Data Science: A Guide for Anyone Who Works with Data

Trevor Hastie, Robert Tibshirani, Jerome Friedman — The Elements of Statistical Learning : Data Mining, Inference and Prediction

Jason Brownlee — Statistical Methods for Machine Learning: Discover How to Transform Data into Knowledge with Python

Ian H. Witten, Eibe Frank, Mark A. Hall, Christopher J. Pal — Data Mining: Practical Machine Learning Tools and Techniques, Fourth Edition

Ian Goodfellow, Yoshua Bengio, Aaron Courville — Deep Learning

Mehryar Mohri, Afshin Rostamizadeh, Ameet Talwalkar — Foundations of Machine Learning, second edition

Christopher M. Bishop — Pattern Recognition and Machine Learning

Larry Wasserman — All of Statistics: A Concise Course in Statistical Inference

James E. Gentle — Matrix Algebra: Theory, Computations and Applications in Statistics

Trevor Hastie, Robert Tibshirani, Jerome Friedman — The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics)

Charles Wheelan — Naked Statistics: Stripping the Dread from the Data

Ruey S. Tsay — Analysis of Financial Time Series, Third Edition (Wiley Series in Probability and Statistics)

Thomas M. Cover, Joy A. Thomas — Elements of Information Theory

E.T. Jaynes; G. Larry Bretthorst (Editor) — Probability Theory: The Logic of Science

David Barber — Bayesian Reasoning and Machine Learning

D. R. Cox — Principles of Statistical Inference

Stephen Boyd, Lieven Vandenberghe — Convex Optimization

George Casella, Roger L. Berger — Statistical Inference

John A. Rice — Mathematical Statistics and Data Analysis 3ed (Duxbury Advanced)

Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani — An Introduction To Statistical Learning: With Applications In R (Second Edition)

Bradley Efron, Trevor Hastie — Computer Age Statistical Inference: Algorithms, Evidence, and Data Science

Marc Peter Deisenroth; A Aldo Faisal; Cheng Soon Ong — Mathematics for Machine Learning

Robert S. Witte, John S. Witte — Statistics

Ruhul Amin Sarker, Charles S. Newton — Optimization modelling: a practical approach

Foster Provost, Tom Fawcett — Data Science for Business: What You Need to Know About Data Mining and Data-Analytic Thinking

Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani — An Introduction To Statistical Learning: With Applications In R

Stephen Marsland — Machine Learning: An Algorithmic Perspective, Second Edition

Allen B. Downey — Think Stats: Exploratory Data Analysis (Second Edition)

Garrett Grolemund, Hadley Wickham — R for Data Science

Jake VanderPlas — Python Data Science Handbook

Peter Bruce, Andrew Bruce — Practical Statistics for Data Scientist

Wes McKinney — Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython

Russell, Matthew A., Klassen, Mikhail — Mining the Social Web: Data Mining Facebook, Twitter, LinkedIn, Instagram, GitHub, and More

Aurélien Géron — Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow

Aurélien Géron — Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow

Joel Grus — Data Science from Scratch: First Principles with Python

Peter Bruce, Andrew Bruce, Peter Gedeck — Practical Statistics for Data Scientists

Arthur Zhang — Data Analytics

Yaser S. Abu-Mostafa, Malik Magdon-Ismail, Hsuan-Tien Lin — Learning From Data: A short course

Peter Harrington — Machine Learning in Action

Most frequently terms