diff --git a/manuscript/00.0-preface.Rmd b/manuscript/00.0-preface.Rmd index b34e5c07..f194f10f 100644 --- a/manuscript/00.0-preface.Rmd +++ b/manuscript/00.0-preface.Rmd @@ -18,6 +18,10 @@ Machine learning has great potential for improving products, processes and resea But **computers usually do not explain their predictions** which is a barrier to the adoption of machine learning. This book is about making machine learning models and their decisions interpretable. +```{r child = 'course.Rmd', eval = is.html} +``` + + After exploring the concepts of interpretability, you will learn about simple, **interpretable models** such as decision trees, decision rules and linear regression. The focus of the book is on model-agnostic methods for **interpreting black box models** such as feature importance and accumulated local effects, and explaining individual predictions with Shapley values and LIME. In addition, the book presents methods specific to deep neural networks. diff --git a/manuscript/course.Rmd b/manuscript/course.Rmd new file mode 100644 index 00000000..66a8f4cb --- /dev/null +++ b/manuscript/course.Rmd @@ -0,0 +1,6 @@ +
+

Interpretable ML Online Course

+

I'm excited to announce that I'm planning an online course on Interpretable Machine Learning. To help me tailor the course to your needs, please take a moment to complete this short survey.

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