update outlook & add ref

book/references.bib

@@ -362,6 +362,15 @@ @misc{jurafsky2024speech
   publisher={Internet: https://web. stanford. edu/\~{} jurafsky/slp3/,[Accessed: June 4, 2023]}
 }
 
+@article{ketkar2021introduction,
+  title={Introduction to pytorch},
+  author={Ketkar, Nikhil and Moolayil, Jojo and Ketkar, Nikhil and Moolayil, Jojo},
+  journal={Deep learning with python: learn best practices of deep learning models with PyTorch},
+  pages={27--91},
+  year={2021},
+  publisher={Springer}
+}
+
 @article{kotsiantis2011combining,
   title={Combining bagging, boosting, rotation forest and random subspace methods},
   author={Kotsiantis, Sotiris},

notebooks/20_machine_learning_ensembles.ipynb

@@ -901,11 +901,11 @@
    "source": [
     "## Outlook: More on Machine Learning\n",
     "\n",
-    "This course is meant as a general introduction to data science. Machine Learning is, no doubt, one of the most essential tools for any data scientist to know about. In fact, it is not just one tool but an entire toolbox full of very powerful methods. It is important to know at least common examples of the most prominent types of tools, including unsupervised methods such as clustering techniques and dimensionality reduction, as well as supervised methods like k-nearest neighbors, linear regression, or decision trees.\n",
+    "This course is meant as a general introduction to data science. Machine Learning is, no doubt, one of the most essential tools for any data scientist. In fact, it is not just one tool but rather an entire toolbox full of very powerful methods. While nobody will be able to be on top of all possible tools in this toolbox, having at least an intuitive understanding of the most prominent types of tools is key for modern data science workflows. This includes unsupervised methods such as clustering techniques and dimensionality reduction, as well as supervised methods like k-nearest neighbors, linear regression, or decision trees.\n",
     "\n",
-    "However, this will just give you a first impression of what is possible with machine learning. In addition, we will focus on a basic intuition and first application of these methods. We will not cover all algorithms in full depth.\n",
+    "We have covered those techniques in this and the prior chapters with exactly this goal in mind. The focus was on a basic intuition and first application of these methods. You will hopefully later realize, that many core concepts apply to other machine learning approaches, too. Still, the extend of machine learning covered in this book is mostly supposed to serve as a good first basis. When you face actual problems that you want to solve using machine learning, you will probably have to expand this basis quiet a bit. Here, we did not cover all algorithms in full depth. And we also had to leave out several common limitations and pitfalls. And, as you probably already know, there are *many* machine learning techniques beyond the ones introduced in the prior chapters, from *support vector machines* all the way to modern *deep learning* approaches.\n",
     "\n",
-    "To deepen your understanding of individual methods and broaden your knowledge on various techniques, you might want to explore further. This includes the large field of deep learning, which we cannot cover in this course.\n",
+    "While we cannot (or do not want) to cover all those techniques in this introduction to data science, there are -luckily- many good resources to help you deepen your understanding of individual methods and broaden your knowledge on various techniques. This includes the large field of deep learning, which is highly relevant for many data science applications, but also something that requires a substantial investment of time to master.\n",
     "\n",
     "### Further Learning Resources\n",
     "\n",
@@ -915,6 +915,7 @@
     "**Books on Machine Learning and Deep Learning:**  \n",
     "- \"Machine Learning with PyTorch and Scikit-Learn: Develop machine learning and deep learning models with Python\" {cite}`raschka2022machine`\n",
     "- \"Understanding Deep Learning\" by Simon Prince, MIT Press, 2023 {cite}`prince2023understanding`\n",
+    "- \"Introduction to pytorch\", {cite}`ketkar2021introduction`\n",
     "\n",
     "By continuing to explore these resources, you can build a solid foundation in machine learning and stay up-to-date with the latest advancements in the field.\n",
     "\n",