Inference in Statistical Modelling and ML by James Burridge (.PDF)
File Size: 106.0 MB
Inference in Statistical Modelling and Machine Learning: A Concise Introduction by James Burridge, Nick Tosh
Requirements: .PDF reader, 106.0 MB
Overview: 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. Our book is aimed at readers coming to the fields of inference, statistical modelling, and Machine Learning for the first time. Prerequisites are therefore minimal: calculus, linear algebra and probability at first-university-course level, and basic Python or R for those keen to dig into the supporting notebooks.
Genre: Non-Fiction > Tech & Devices
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