Inference in Statistical Modelling and Machine Learning: A Concise Introduction

Inference in Statistical Modelling and Machine Learning: A Concise Introduction book cover

Inference in Statistical Modelling and Machine Learning: A Concise Introduction

Author(s): James Burridge (Author), Nick Tosh (Author)

  • Publisher: Cambridge University Press
  • Publication Date: June 30, 2026
  • Language: English
  • Print length: 323 pages
  • ISBN-10: 1009630687
  • ISBN-13: 9781009630689

Book Description

Statistical modelling and machine learning offer a vast toolbox of inference methods with which to model the world, discover patterns and reach beyond the data to make predictions when the truth is not certain. This concise book provides a clear introduction to those tools and to the core ideas – probabilistic model, likelihood, prior, posterior, overfitting, underfitting, cross-validation – that unify them. Toy and real examples illustrate diverse applications ranging from biomedical data to treasure hunts, while the accompanying datasets and computational notebooks in R and Python encourage hands-on learning. Instructors can benefit from online lecture slides and solutions to all the exercises. Requiring only first-year university-level knowledge of calculus, probability and linear algebra, the book equips students in statistics, data science and machine learning, as well as those in quantitative applied and social science programmes, with the tools and conceptual foundations to explore more advanced techniques.

Editorial Reviews

Review

‘Burridge and Tosh provide an accessible but detailed introduction to probability, model fitting, and machine learning. Filled with offbeat examples, this is essential reading for anyone aspiring to work in data science, artificial intelligence, or indeed any branch of experimental science or engineering.’ Simon J. D. Prince, University of Bath

Book Description

A concise introduction to core ideas in statistical modelling and machine learning, preparing you to progress to more advanced techniques.

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