Abu-Mostafa YS, Magdon-Ismail M and Lin H-T, Learning from Data: A Short Course (AMLBook.com 2012)
Barber D, Bayesian Reasoning and Machine Learning (Cambridge University Press 2012)
Bishop CM, Pattern Recognition and Machine Learning, vol Information science and statistics (Springer 2006)
Casella G and Berger RL, Statistical Inference, vol The Duxbury advanced series in statistics and decision sciences (Second edition, Cengage Learning 2017)
Goodfellow I, Bengio Y and Courville A, Deep Learning, vol Adaptive computation and machine learning (The MIT Press 2016)
Grimmett G and Stirzaker D, Probability and Random Processes (Third edition, Oxford University Press 2001)
Harrington P, Machine Learning in Action (Manning Publications 2012)
Hastie T, Tibshirani R and Friedman JH, The Elements of Statistical Learning: Data Mining, Inference, and Prediction : With 200 Full-Color Illustrations, vol Springer series in statistics (Springer 2001)
——, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, vol Springer series in statistics (2nd ed, Springer 2009)
Kabacoff R, R in Action: Data Analysis and Graphics with R (Second edition, Manning 2015)
Karau H and others, Learning Spark: Lightning-Fast Big Data Analytics (O’Reilly 2013) <https://www.vlebooks.com/vleweb/product/openreader?id=UniLincoln&isbn=9781449359065>
‘Kevin Sheppard - Lecture Notes’ <https://www.kevinsheppard.com/Main_Page>
Kiusalaas J, Numerical Methods in Engineering with MATLAB (Third edition, Cambridge University Press 2016)
Lantz B, Machine Learning with R: Learn How to Use R to Apply Powerful Machine Learning Methods and Gain an Insight into Real-World Applications (Packt Publishing Limited 2013) <https://www.vlebooks.com/vleweb/product/openreader?id=UniLincoln&isbn=9781782162155>
Martinez WL and Martinez AR, Computational Statistics Handbook with MATLAB, vol Chapman&Hall/CRC computer science and data analysis series (Third edition, Chapman & Hall/CRC 2016)
McKinney W, Python for Data Analysis (O’Reilly 2013) <http://proxy.library.lincoln.ac.uk/login?url=http://www.dawsonera.com/depp/reader/protected/external/AbstractView/S9781449323622>
Mood AM, Graybill FA and Boes DC, Introduction to the Theory of Statistics, vol McGraw-Hill series in probability and statistics (Third edition, McGraw-Hill Book Company 1974)
Murphy KP, Machine Learning: A Probabilistic Perspective, vol Adaptive computation and machine learning (MIT Press 2012)
Nolan DA and Lang DT (eds), Data Science in R: A Case Studies Approach to Computational Reasoning and Problem Solving, vol Chapman&Hall/CRC the R series (Chapman & Hall/CRC 2015)
Peng R, R Programming for Data Science (Lulu.com 2016)
Raschka S, Python Machine Learning: Unlock Deeper Insights into Machine Learning with This Vital Guide to Cutting-Edge Predictive Analytics, vol Community experience distilled (Packt Publishing 2015)
Sarkar D, Lattice: Multivariate Data Visualization with R, vol Use R! (Springer 2008) <https://www.vlebooks.com/vleweb/product/openreader?id=UniLincoln&isbn=9780387759692>
‘Source Code for the Book: Machine Learning in Action Published by Manning’ <https://github.com/pbharrin/machinelearninginaction>
Wickham H, Ggplot2: Elegant Graphics for Data Analysis, vol Use R! (Springer 2009) <https://www.vlebooks.com/vleweb/product/openreader?id=UniLincoln&isbn=9780387981413>
——, Advanced R, vol Chapman&Hall/CRC the R series (Chapman & Hall/CRC 2014)
——, Advanced R, vol Chapman&Hall/CRC the R series (Chapman & Hall/CRC 2014)
——, R Packages (O’Reilly Media 2015)
Zumel N and Mount J, Practical Data Science with R (Manning 2014)