Price loading...

Springer Applied Machine Learning

Name: Applied Machine Learning
Brand: Springer

AI & Machine Learning

Price History & Forecast

No Price Data Available

Price history will appear here once data is collected from Amazon.

Price Distribution

No price data available for histogram

Description

Machine learning methods are now an important tool for scientists, researchers, engineers and students in a wide range of areas. This book is written for people who want to adopt and use the main tools of machine learning, but aren’t necessarily going to want to be machine learning researchers. Intended for students in final year undergraduate or first year graduate computer science programs in machine learning, this textbook is a machine learning toolkit. Applied Machine Learning covers many topics for people who want to use machine learning processes to get things done, with a strong emphasis on using existing tools and packages, rather than writing one’s own code. A companion to the author's Probability and Statistics for Computer Science, this book picks up where the earlier book left off (but also supplies a summary of probability that the reader can use). Emphasizing the usefulness ofstandard machinery from applied statistics, this textbook gives an overview of the major applied areas in learning, including coverage of:• classification using standard machinery (naive bayes; nearest neighbor; SVM)• clustering and vector quantization (largely as in PSCS)• PCA (largely as in PSCS)• variants of PCA (NIPALS; latent semantic analysis; canonical correlation analysis)• linear regression (largely as in PSCS)• generalized linear models including logistic regression• model selection with Lasso, elasticnet• robustness and m-estimators• Markov chains and HMM’s (largely as in PSCS)• EM in fairly gory detail; long experience teaching this suggests one detailed example is required, which students hate; but once they’ve been through that, the next one is easy• simple graphical models (in the variational inference section)• classification with neural networks, with a particular emphasis onimage classification• autoencoding with neural networks• structure learning

Product Specifications

Brand: Springer
Format: paperback
ASIN: 3030181162
Category: Books > Subjects > Computing & Internet > Computer Science > AI & Machine Learning
Domain: Amazon UK
Release Date: 14 August 2020
Listed Since: 29 June 2020

Barcode

No barcode data available

Springer Applied Machine Learning

Price History & Forecast

No Price Data Available

Price Distribution

Description

Product Specifications

Barcode

Similar Products You Might Like

Probability and Statistics for Computer Science

Probability and Statistics for Machine Learning: A Textbook

Applied Statistical Learning: With Case Studies in Stata (Statistics and Computing)

Applied Statistical Learning: With Case Studies in Stata (Statistics and Computing)

Machine Learning: A Practical Approach on the Statistical Learning Theory

Machine Learning: A Bayesian and Optimization Perspective

Probability for Statistics and Machine Learning: Fundamentals and Advanced Topics (Springer Texts in Statistics)

Data Science and Predictive Analytics: Biomedical and Health Applications using R (The Springer Series in Applied Machine Learning)

Machine Learning Algorithms: Popular algorithms for data science and machine learning, 2nd Edition

An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics)

Data Science and Predictive Analytics: Biomedical and Health Applications using R (The Springer Series in Applied Machine Learning)

Fundamentals of Pattern Recognition and Machine Learning

Machine Learning: Theory and Practice

Machine Learning Methods

Applied Predictive Modeling

Machine Learning – A Probabilistic Perspective

Machine Learning and its Applications

Applied Machine Learning for Data Science Practitioners

Machine Learning and Big Data: Concepts, Algorithms, Tools and Applications

Linear Algebra and Optimization for Machine Learning: A Textbook

Machine Learning with R

Machine Learning

Machine Learning

Pattern Recognition and Machine Learning (Information Science and Statistics)