Linear Modelling: A Least Squares Approach. Linear Modelling: A Maximum Likelihood Approach. The Bayesian Approach to Machine Learning. Bayesian Inference. Classification. Clustering. Principal Components Analysis and Latent Variable Models. Further Topics in Markov Chain Monte Carlo. Classification and Regression with Gaussian Processes. Dirichlet Process models.
Simon Rogers is a lecturer in the School of Computing Science at the University of Glasgow, where he teaches a masters-level machine learning course on which this book is based. Dr. Rogers is an active researcher in machine learning, particularly applied to problems in computational biology. His research interests include the analysis of metabolomic data and the application of probabilistic machine learning techniques in the field of human-computer interaction.
Mark Girolami holds an honorary professorship in Computer Science at the University of Warwick, is an EPSRC Established Career Fellow (2012 - 2017) and previously an EPSRC Advanced Research Fellow (2007 - 2012). He is also honorary Professor of Statistics at University College London, is the Director of the EPSRC funded Research Network on Computational Statistics and Machine Learning and in 2011 was elected to the Fellowship of the Royal Society of Edinburgh when he was also awarded a Royal Society Wolfson Research
"I was impressed by how closely the material aligns with the needs
of an introductory course on machine learning, which is its
greatest strength. While there are other books available that aim
for completeness, with exhaustively comprehensive introductions to
every branch of machine learning, the book by Rogers and Girolami
starts with the basics, builds a solid and logical foundation of
methodology, before introducing some more advanced topics. The
essentials of the model construction, validation, and evaluation
process are communicated clearly and in such a manner as to be
accessible to the student taking such a course. I was also pleased
to see that the authors have not shied away from producing
algebraic derivations throughout, which are for many students an
essential part of the learning process—many other texts omit such
details, leaving them as ‘an exercise for the reader.’ Being shown
the explicit steps required for such derivations is an important
part of developing a sense of confidence in the student. Overall,
this is a pragmatic and helpful book, which is well-aligned to the
needs of an introductory course and one that I will be looking at
for my own students in coming months."
—David Clifton, University of Oxford, UK"In my opinion, this is by
far the best introduction to Machine Learning. It accomplishes
something I would think impossible: it assumes essentially only
high school mathematics and no statistics background, and yet, by
introducing math, probability and statistics as needed, it manages
to do an entirely rigorous introduction to Machine Learning. Proofs
are not provided only for very few theorems; the book goes fairly
deep and is really enjoyable to read. I told my students that this
book will be one of the best investments they have ever made!"
—Aleksandar Ignjatovic, University of New South Wales"The new
edition of A First Course in Machine Learning by Rogers and
Girolami is an excellent introduct
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