{"id":12074,"date":"2021-03-06T09:16:19","date_gmt":"2021-03-06T09:16:19","guid":{"rendered":"https:\/\/analystprep.com\/study-notes\/?p=12074"},"modified":"2025-12-18T16:37:10","modified_gmt":"2025-12-18T16:37:10","slug":"supervised-machine-learning-unsupervised-machine-learning-deep-learning","status":"publish","type":"post","link":"https:\/\/analystprep.com\/study-notes\/cfa-level-2\/quantitative-method\/supervised-machine-learning-unsupervised-machine-learning-deep-learning\/","title":{"rendered":"Supervised Machine Learning, Unsupervised Machine Learning, and Deep Learning"},"content":{"rendered":"<p><iframe loading=\"lazy\" width=\"560\" height=\"315\" src=\"https:\/\/www.youtube.com\/embed\/JOpNoBlW_0Q?si=wB8tY-TDqL1AFdDL\" title=\"YouTube video player\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" allowfullscreen><\/iframe><br \/>\n<script type=\"application\/ld+json\">\n{\n  \"@context\": \"https:\/\/schema.org\",\n  \"@type\": \"ImageObject\",\n  \"url\": \"https:\/\/analystprep.com\/study-notes\/wp-content\/uploads\/2021\/03\/Img_23.jpg\",\n  \"caption\": \"Machine learning Techniques\",\n  \"width\": 1590,\n  \"height\": 1119,\n  \"copyrightNotice\": \"\u00a9 2024 AnalystPrep\",\n  \"acquireLicensePage\": \"https:\/\/analystprep.com\/license-info\",\n  \"creditText\": \"AnalystPrep Design Team\",\n  \"creator\": {\n    \"@type\": \"Organization\",\n    \"name\": \"AnalystPrep\"\n  }\n}\n<\/script><\/p>\n<p><script type=\"application\/ld+json\">\n{\n  \"@context\": \"https:\/\/schema.org\",\n  \"@type\": \"ImageObject\",\n  \"url\": \"https:\/\/analystprep.com\/study-notes\/wp-content\/uploads\/2021\/03\/Img_12.jpg\",\n  \"caption\": \"Supervised Vs. Unsupervised Learning\",\n  \"width\": 1590,\n  \"height\": 794,\n  \"copyrightNotice\": \"\u00a9 2024 AnalystPrep\",\n  \"acquireLicensePage\": \"https:\/\/analystprep.com\/license-info\",\n  \"creditText\": \"AnalystPrep Design Team\",\n  \"creator\": {\n    \"@type\": \"Organization\",\n    \"name\": \"AnalystPrep\"\n  }\n}\n<\/script><\/p>\n<p><script type=\"application\/ld+json\">\n{\n  \"@context\": \"https:\/\/schema.org\",\n  \"@type\": \"QAPage\",\n  \"mainEntity\": {\n    \"@type\": \"Question\",\n    \"name\": \"Which of the following statements most likely differentiates supervised learning from unsupervised learning?\",\n    \"acceptedAnswer\": {\n      \"@type\": \"Answer\",\n      \"text\": \"The correct answer is A.\\n\\nSupervised learning uses a labeled data set to infer the patterns between the inputs and outputs.\"\n    },\n    \"suggestedAnswer\": [\n      {\n        \"@type\": \"Answer\",\n        \"text\": \"A. Using labeled data to infer the patterns between the inputs and outputs.\"\n      },\n      {\n        \"@type\": \"Answer\",\n        \"text\": \"B. Using unlabeled data to infer the patterns between the inputs and outputs.\"\n      },\n      {\n        \"@type\": \"Answer\",\n        \"text\": \"C. Using existing data to train algorithms to establish patterns and then use that to make predictions about new data.\"\n      }\n    ],\n    \"answerCount\": 3\n  }\n}\n<\/script><\/p>\n<p>Machine learning employs the idea that systems can learn from data, identify patterns, and make decisions with minimal human intervention. It has an iterative aspect in that when models are exposed to new data, they can adapt independently.<\/p>\n<p>Machine learning has several branches, which include; supervised learning, unsupervised learning, and deep learning, and reinforcement learning.<\/p>\n<h2>Supervised Learning<\/h2>\n<p>With supervised learning, the algorithm is given a set of particular targets to aim for. Supervised learning uses <strong>labeled data set, <\/strong>one that contains matched sets of observed inputs, X\u2019s, and the associated outputs, Y\u2019s. The algorithm is \u201ctrained,\u201d i.e., the machine learning algorithm is applied to this data set to infer the patterns between the inputs and outputs. In other words, the algorithm works out \\(f\\) in the equation:<\/p>\n<p>$$Y=f(X)$$<\/p>\n<p>The dependent variable (Y) is the <strong>target, <\/strong>while the independent variables (X\u2019s) are known as <strong>features.<\/strong><\/p>\n<h3>Types of Supervised Learning<\/h3>\n<p>Supervised learning can be categorized into two problems, depending on the nature of the target (Y) variable. These include <strong>classification<\/strong> and <strong>regression. <\/strong><\/p>\n<p><strong>Classification<\/strong> focuses on sorting observations into distinct categories. It is when the target (Y) is a category, such as \u201cblue\u201d or \u201cred\u201d or \u201cdisease\u201d and \u201cno disease.\u201d An example of a classification problem is a spam filter that classifies emails into two categories, \u2018safe\u2019 or \u2018suspicious.\u2019<\/p>\n<p><strong>Regression<\/strong> is based on making predictions of continuous target variables. Examples of regression problems include using historical stock market returns to forecast stock price performance or predicting the number of goals scored by a team.<\/p>\n<h2>Unsupervised Learning<\/h2>\n<p>The algorithm aims to produce a set of suitable labels (targets) under unsupervised learning. In other words, we have inputs (X\u2019s) that are used for analysis with no corresponding target (Y). Unsupervised learning seeks to model the underlying structure or distribution in the data to learn more about the data since it is not given labeled training data. It is, therefore, useful for exploring new data sets that are large and complex.<\/p>\n<h3>Types of Unsupervised Learning<\/h3>\n<p>Two essential types of unsupervised learning are <strong>dimension reduction<\/strong> and <strong>clustering<\/strong>.<\/p>\n<p><strong>Dimension reduction<\/strong> refers to reducing the number of inputs (features) while retaining variation across observations to maintain structure and usefulness of the information contained in the variation. For example, data scientists reduce the number of dimensions in an extensive data set to simplify modeling and reduce file size.<\/p>\n<p>On the other hand, <strong>clustering<\/strong> seeks to sort observations into clusters (groups) such that the observations within a cluster are similar, while those in different clusters are dissimilar. For example, asset managers use clustering to sort companies into empirically determined groupings rather than conventional groupings, i.e., based on their financial statement data rather than sectors or countries.<\/p>\n<p>The following displays the difference between <b>supervised learning <\/b>and <b>unsupervised learning<\/b>:<\/p>\n<h2><img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-full wp-image-14888\" src=\"https:\/\/analystprep.com\/study-notes\/wp-content\/uploads\/2021\/03\/Img_12.jpg\" alt=\"Supervised vs. Unsupervised Learning\" width=\"1590\" height=\"794\" srcset=\"https:\/\/analystprep.com\/study-notes\/wp-content\/uploads\/2021\/03\/Img_12.jpg 1590w, https:\/\/analystprep.com\/study-notes\/wp-content\/uploads\/2021\/03\/Img_12-300x150.jpg 300w, https:\/\/analystprep.com\/study-notes\/wp-content\/uploads\/2021\/03\/Img_12-1024x511.jpg 1024w, https:\/\/analystprep.com\/study-notes\/wp-content\/uploads\/2021\/03\/Img_12-768x384.jpg 768w, https:\/\/analystprep.com\/study-notes\/wp-content\/uploads\/2021\/03\/Img_12-1536x767.jpg 1536w, https:\/\/analystprep.com\/study-notes\/wp-content\/uploads\/2021\/03\/Img_12-800x400.jpg 800w, https:\/\/analystprep.com\/study-notes\/wp-content\/uploads\/2021\/03\/Img_12-400x200.jpg 400w\" sizes=\"auto, (max-width: 1590px) 100vw, 1590px\" \/>Deep Learning and Reinforcement Learning<\/h2>\n<p>Deep learning and reinforcement learning functions enable a computer to develop rules on its own to solve problems.<\/p>\n<p><strong>Deep learning<\/strong> is a self-teaching system in which the existing data is used to train algorithms to establish patterns and then use that to make predictions about new data. Highly complex tasks, such as image classification, face recognition, speech recognition, and natural language processing, are addressed by sophisticated algorithms.<\/p>\n<p>In <strong>reinforcement learning<\/strong>, a computer learns from trial and error. It learns dynamically by adjusting actions based on continuous feedback to maximize an outcome.<\/p>\n<p>Deep learning and reinforcement learning are based on neural networks (NNs, also called artificial neural networks or ANNs). NNs include highly flexible ML algorithms that have been successfully applied to a variety of tasks characterized by nonlinearities and interactions among features.<\/p>\n<p>The following chart summarizes the discussion on machine learning techniques:<\/p>\n<blockquote>\n<h2><img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-full wp-image-14890\" src=\"https:\/\/analystprep.com\/study-notes\/wp-content\/uploads\/2021\/03\/Img_23.jpg\" alt=\"Machine Learning Techniques\" width=\"1590\" height=\"1119\" srcset=\"https:\/\/analystprep.com\/study-notes\/wp-content\/uploads\/2021\/03\/Img_23.jpg 1590w, https:\/\/analystprep.com\/study-notes\/wp-content\/uploads\/2021\/03\/Img_23-300x211.jpg 300w, https:\/\/analystprep.com\/study-notes\/wp-content\/uploads\/2021\/03\/Img_23-1024x721.jpg 1024w, https:\/\/analystprep.com\/study-notes\/wp-content\/uploads\/2021\/03\/Img_23-768x540.jpg 768w, https:\/\/analystprep.com\/study-notes\/wp-content\/uploads\/2021\/03\/Img_23-1536x1081.jpg 1536w, https:\/\/analystprep.com\/study-notes\/wp-content\/uploads\/2021\/03\/Img_23-400x282.jpg 400w\" sizes=\"auto, (max-width: 1590px) 100vw, 1590px\" \/>Question<\/h2>\n<p>Which of the following statements <em>most likely<\/em> differentiates supervised learning from unsupervised learning?<\/p>\n<p>Supervised learning involves:<\/p>\n<p>\u00a0 \u00a0 \u00a0 A. Using labeled data to infer the patterns between the inputs and outputs.<\/p>\n<p>\u00a0 \u00a0 \u00a0 B. Using unlabeled data to infer the patterns between the inputs and outputs.<\/p>\n<p>\u00a0 \u00a0 \u00a0 C. Using existing data to train algorithms to establish patterns and then use that to make predictions about new data.<\/p>\n<h3>Solution<\/h3>\n<p><strong>The correct answer is A.<\/strong><\/p>\n<p>Supervised learning uses a <strong>labeled data set <\/strong>to infer the patterns between the inputs and outputs.<\/p>\n<p><strong>B is incorrect.<\/strong>\u00a0Option B describes unsupervised learning. With unsupervised learning, inputs (X\u2019s) are used for analysis with no corresponding target (Y). Unsupervised learning seeks to model the underlying structure or distribution in the data to learn more about the data since it is not given labeled training data.<\/p>\n<p><strong>C is incorrect. <\/strong>Using existing data to train algorithms to establish patterns and then use those patterns to make predictions about new data best describes deep learning.<\/p>\n<\/blockquote>\n<p>Reading 6: Machine Learning<\/p>\n<p><em>LOS 6 (a) Describe supervised machine learning, unsupervised machine learning, and deep learning.<\/em><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Machine learning employs the idea that systems can learn from data, identify patterns, and make decisions with minimal human intervention. It has an iterative aspect in that when models are exposed to new data, they can adapt independently. Machine learning&#8230;<\/p>\n","protected":false},"author":5,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[102,229],"tags":[255,216,230,253,254],"class_list":["post-12074","post","type-post","status-publish","format-standard","hentry","category-cfa-level-2","category-quantitative-method","tag-and-deep-learning","tag-cfa-level-2","tag-quantitative-method","tag-supervised-machine-learning","tag-unsupervised-machine-learning","blog-post","no-post-thumbnail","animate"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.4 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Supervised Machine Learning, Unsupervised Machine Learning, and Deep Learning - CFA, FRM, and Actuarial Exams Study Notes<\/title>\n<meta name=\"description\" content=\"Explore how these techniques use data to infer patterns, make predictions, and solve complex problems with minimal human intervention.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/analystprep.com\/study-notes\/cfa-level-2\/quantitative-method\/supervised-machine-learning-unsupervised-machine-learning-deep-learning\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Supervised Machine Learning, Unsupervised Machine Learning, and Deep Learning - 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