Chapter 1 A brief introduction to machine learning and multivariate statistics

An introduction to Machine Learning and Multivariate Statistics – video

An introduction to R – video

Chapter 2 Matrix Algebra

Basic matrix operations

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• Matrices and matrix operations, part 1 – video

Determinant and inverse of a matrix

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• Matrices and matrix operations, part 2 – video

Eigenvectors and eigenvalues

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• Eigenvectors and eigenvalues- video
• Eigenvectors and eigenvalues – the math (optional) – video

Chapter 3 Managing data in R

Working with data frames

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Generate random data

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Missing data and imputation

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Datasets used in this book

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Chapter 4 Graphical illustration of multivariate data

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Chapter 5 Covariance and the correlation matrix

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• Covariance and the correlation matrix – video

Chapter 6 PCA and PCoA

Principal component analysis

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• PCA: the basics – video
• PCA – the math (optional) – video

Standardization and how to extract components

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• PCA: standardization and how to extract components – video

Loadings and Varimax rotation

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• PCA: how to interpret the weights/loadings and Varimax rotation – video

Principal Coordinates Analysis (PCoA)

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• PCoA and classical multidimensional scaling – video

Chapter 7 Linear discriminant analysis

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• Linear discriminant analysis (LDA) – video

Chapter 8 Distances in space

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• Euclidean distance and the Mahalanobis distance – video

Chapter 9 Multivariate statistical tests

Hotelling’s T-square and MANOVA

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• MANOVA – video
• Hotelling’s T-square – video

PERMANOVA

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• PERMANOVA – video

Canonical correlation analysis

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• Canonical correlation analysis (CCA) – video

Chapter 10 Classification and performance metrics

Diagnostic Metrics

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• Sensitivity and specificity – video
• The positive and negative predictive values – video
• The likelihood ratio – video

ROC curve

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• ROC curve – video

Validation

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• Validation techniques – video

Chapter 11 Supervised Machine Learning

Validation and evaluation

Linear discriminant analysis

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• LDA – how to use it as a classifier – video

Logistic regression

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• Logistic regression: the basics – video
• Logistic regression: how to use it as a classifier – video

Decision trees

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• Decision trees – video

Random forest

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• Random forest – video

k-Nearest Neighbors

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• k-nearest neighbors – video

Gaussian Naive Bayes

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• Gaussian naive Bayes – video

The problem with imbalanced datasets in ML

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Chapter 12 Clustering

Hierarchical clustering

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• Hierarchical clustering – video

Heatmap

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• Understanding cluster heat maps – video

K-Means clustering

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• k-means clustering – video

Chapter 13 PCR, PLS and Lasso regression

Principal component regression and Partial least squares regression

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• Principal component regression – video
• Bootstrap confidence intervals – video
• Partial least squares regression – video

Lasso regression

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• Lasso regression – video

Chapter 14 Case studies

Paper 1

Dataset: Cytokines

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Paper 2

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• Kaplan-Meier analysis – video
• The log-rank test – video
• Linear mixed-effects model, part 1 – video
• Linear mixed-effects model, part 2 – video
• Cox proportional hazards model – video

Chapter 15 Answers to the exercises

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