Elements of data science, machine learning, and artificial intelligence using R

Elements of data science, machine learning, and artificial intelligence using R / Frank Emmert-Streib, Salissou Moutari, Matthias Dehmer.

In recent years, large amounts of data became available in all areas of science, industry and society. This provides unprecedented opportunities for enhancing our knowledge, and to solve scientific and societal problems. In order to emphasize the importance of this, data have been called the "o...

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Bibliographic Details
Main Authors: Emmert-Streib, Frank (Author), Moutari, Salissou (Author), Dehmer, Matthias, 1968- (Author)
Format: eBook
Language:English
Published: Cham : Springer, [2023]
Subjects:
Artificial intelligence.
Machine learning.
R (Computer program language)
artificial intelligence.
Artificial intelligence
Machine learning
Online Access:Click for online access
Table of Contents:
  • Introduction
  • Introduction to learning from data
  • Part 1: General topics
  • Prediction models
  • Error measures
  • Resampling
  • Data types
  • Part 2: Core methods
  • Maximum Likelihood & Bayesian analysis
  • Clustering
  • Dimension Reduction
  • Classification
  • Hypothesis testing
  • Linear Regression
  • Model Selection
  • Part 3: Advanced topics
  • Regularization
  • Deep neural networks
  • Multiple hypothesis testing
  • Survival analysis
  • Generalization error
  • Theoretical foundations
  • Conclusion.

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