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<h1>Welcome to our Principles of Machine Learning</h1>
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<h1>Welcome to our Principles of Machine Learning<a class="headerlink" href="#welcome-to-our-principles-of-machine-learning" title="Permalink to this heading">#</a></h1>
<p>The popularity of machine learning has grown tremendously during the past two decades. Every day new scientific publications, blog entries, textbooks and media articles are published covering machine learning topics; novel products and services built on machine learning approaches are announced; thousands of students enthusiastically enrol onto a machine learning course for the very first time. Due to its popularity the availability of machine learning resources is, effectively, unlimited. This being the scene we, as the authors of this book, owe you an explanation. Why are we offering yet another title on machine learning? And what do we mean by a <em>deployment-first perspective</em>?</p>
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<p>Putting it simply, we have tried to write the book that we have wished our machine learning students had. Our students start their machine learning journey equipped a wide variety of skills and backgrounds: physics, maths, engineering, computer science, medicine, sociology, philosophy, you name it. Some of them start their machine learning journey with us, whereas others have already started their journey on their own, using the vast amount of material that is available. As academics we have our favourite textbooks, the ones we keep coming back to for deeper insight and inspiration. They are the right textbooks for us, but not quite the right choice for our students. Some of these textbooks rely heavily on mathematical notation and analysis and implicitly assume a scientific background in their readers. Others follow applied, hands-on approaches and touch on the fundamentals of machine learning only superficially. In either case, the principles of machine learning remain inaccessible to many of our students. Furthermore, regardless of their orientation, most textbooks use what we will call a <strong>dataset-first perspective</strong> of machine learning, according to which machine learning starts with a dataset. We believe this is not the most enlightening way to discuss machine learning.</p>
<p>Reading the words <em>principles</em> and <em>deployment</em> in the same line might have raised a few eyebrows. The term <em>principles</em> suggests that we are abstracting away application-specific details and instead focus on that essence that is common to many, superficially different problems. In contrast, the term <em>deployment</em> suggests that application-specific details are relevant and hence cannot be abstracted away. In this book we use the term <em>deployment</em> in an abstract sense, to refer to the process by which an already built system, in our case a <em>machine learning system</em>, is put to work. Building and deployment are two stages that form the <strong>machine learning lifecycle</strong> and therefore any discussion around the principles of machine learning should <strong>conceptualise both stages</strong>. We go much further than simply recognising both stages: we believe that machine learning is best understood if we explicitly think about deployment, not just building. In our view of machine learning, which we call the <strong>deployment-first perspective</strong>, deployment considerations come first, datasets second.</p>
<p>In this book you will learn about the principles of machine learning, its scope and applications. The techniques that we will discuss do not differ from techniques that you will find in other textbooks, however we believe our deployment-first angle will enhance your understanding. Our goal is that after reading this book you will be able to apply the machine learning methodology to build and rigorously evaluate machine learning systems and furthermore, that you will be able to independently learn and apply new machine learning techniques, and to critically analyse reports on machine learning applications and advances. Crucially, you will learn when it makes sense to use machine learning and when it does not, what machine learning can do and cannot. Finally, we hope that you will enjoy learning about machine learning: we believe it is more about humans, than about machines.</p>
<p>We dedicate this book to all our past and current students.</p>
<p>This book is organised in the following chapters:</p>
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<li class="toctree-l1"><a class="reference internal" href="Ch_introduction.html">1. Introduction</a></li>
<li class="toctree-l1"><a class="reference internal" href="Ch_regression.html">2. Regression</a></li>
<li class="toctree-l1"><a class="reference internal" href="Ch_methodology1.html">3. Methodology I: Three basic tasks</a></li>
<li class="toctree-l1"><a class="reference internal" href="Ch_classification1.html">4. Classification I: The geometric view</a></li>
<li class="toctree-l1"><a class="reference internal" href="Ch_discovery.html">5. Structure analysis</a></li>
<li class="toctree-l1"><a class="reference internal" href="Ch_density.html">6. Density estimation</a></li>
<li class="toctree-l1"><a class="reference internal" href="Ch_classification2.html">7. Classification II: The probabilistic view</a></li>
<li class="toctree-l1"><a class="reference internal" href="Ch_methodology2.html">8. Methodology II: Pipelines</a></li>
<li class="toctree-l1"><a class="reference internal" href="Ch_feature.html">9. Feature Engineering</a></li>
<li class="toctree-l1"><a class="reference internal" href="Ch_ensemble.html">10. Ensemble methods</a></li>
<li class="toctree-l1"><a class="reference internal" href="Ch_neuralnets.html">11. Neural networks</a></li>
<li class="toctree-l1"><a class="reference internal" href="Ch_optimisation.html">12. Optimisation methods</a></li>
<li class="toctree-l1"><a class="reference internal" href="Ch_methodology3.html">13. Methodology III: Workflows</a></li>
<li class="toctree-l1"><a class="reference internal" href="Ch_ethics.html">14. The machine learning professional</a></li>
<li class="toctree-l1"><a class="reference internal" href="Ch_appendix.html">15. Appendix</a></li>
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<p>They are meant to be read in the order in which they appear, starting with the introduction.</p>
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