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chapters/01_introduction.html

@@ -192,7 +192,7 @@ <h1 class="title"><span class="chapter-number">1</span>&nbsp; <span class="chapt
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-<p><a href="https://docs.google.com/presentation/d/1hQQVCuGFL5nYssFba1ZK_J-HxGiiRreV9_wx03NlPM4/edit?usp=sharing">Reader</a></p>
+<p><a href="https://docs.google.com/presentation/d/1hQQVCuGFL5nYssFba1ZK_J-HxGiiRreV9_wx03NlPM4/edit?usp=sharing">Slides</a></p>
 
 
 

search.json

@@ -14,7 +14,7 @@
     "href": "chapters/01_introduction.html",
     "title": "1  Introduction",
     "section": "",
-    "text": "This chapter provides an overview of contemporary machine learning. We’ll cover important terminology and popular methods so that you can determine whether machine learning is relevant to your research and what to learn more about if it is. This is a concept-focused, non-technical chapter.\n\n\n\n\n\n\nLearning Goals\n\n\n\nAfter this workshop, learners should be able to:\n\nDefine the following terms: observation, feature, machine learning, supervised learning, unsupervised learning, regression, classification, clustering, training set, validation set, test set, cross-validation, overfitting, underfitting, model bias, model variance, bias-variance tradeoff, ensemble model.\nExplain the difference between supervised and unsupervised learning.\nExplain the difference between regression and classification.\nList and briefly describe popular machine learning methods.\nGive an example of an ensemble model.\nExplain what cross-validation is used for and give an overview of the procedure.\nAssess whether and which machine learning methods might be helpful for a given research problem.\n\n\n\nReader",
+    "text": "This chapter provides an overview of contemporary machine learning. We’ll cover important terminology and popular methods so that you can determine whether machine learning is relevant to your research and what to learn more about if it is. This is a concept-focused, non-technical chapter.\n\n\n\n\n\n\nLearning Goals\n\n\n\nAfter this workshop, learners should be able to:\n\nDefine the following terms: observation, feature, machine learning, supervised learning, unsupervised learning, regression, classification, clustering, training set, validation set, test set, cross-validation, overfitting, underfitting, model bias, model variance, bias-variance tradeoff, ensemble model.\nExplain the difference between supervised and unsupervised learning.\nExplain the difference between regression and classification.\nList and briefly describe popular machine learning methods.\nGive an example of an ensemble model.\nExplain what cross-validation is used for and give an overview of the procedure.\nAssess whether and which machine learning methods might be helpful for a given research problem.\n\n\n\nSlides",
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       "<span class='chapter-number'>1</span>  <span class='chapter-title'>Introduction</span>"
     ]