{"id":1897,"date":"2019-04-07T12:00:00","date_gmt":"2019-04-07T10:00:00","guid":{"rendered":"https:\/\/kindsonthegenius.com\/blog\/machine-learning-101-classes-of-machine-learning-problems\/"},"modified":"2026-07-05T03:22:33","modified_gmt":"2026-07-05T01:22:33","slug":"machine-learning-101-classes-of-machine-learning-problems","status":"publish","type":"post","link":"https:\/\/kindsonthegenius.com\/blog\/machine-learning-101-classes-of-machine-learning-problems\/","title":{"rendered":"Machine Learning 101 \u2013 Classes of Machine Learning Problems"},"content":{"rendered":"

This is lesson 3 of Machine Learning 101.
\nWe are going to examine classes of machine learning problems.<\/p>\n

In Lecture 2<\/a> we already mentioned a handwriting recognition problem. This is an example of supervised learning problem.<\/p>\n

    \n
  1. Supervised Learning<\/a><\/li>\n
  2. Unsupervised Learning<\/a><\/li>\n
  3. Reinforcement Learning<\/a><\/li>\n<\/ol>\n

     <\/p>\n

    1. Supervised Learning<\/strong><\/h4>\n

    In supervised learning problems, you are given a set of data (training data) which is made up of set of input vectors. Then you are also given a set of outputs, or target vectors (that is the actual numbers).<\/p>\n

    Now, supervised learning is divided into two categories: classification<\/em> and regression<\/strong><\/p>\n

    In a case such as the handwritten digit recognition, where the objective is to assign each input vector to on of a finite set of discreet categories (numbers from 0 to 9), then this is called classification.<\/p>\n

    However, if the output is made up of a number of continuous variables (heights of people), then this is a regression problem. An example of regression is to predict the volume of sales for a marketing firm if certain amount was spend on an ad campaign in TV and newspaper.<\/p>\n

    So now, let’s summarize what you’ve learnt:<\/p>\n

      \n
    • Machine learning is divided into two categories: supervised learning and unsupervised learning.<\/li>\n
    • Supervised learning is divided into two: classification and regression.<\/li>\n<\/ul>\n

      Let’s now talk about unsupervised learning<\/p>\n

      \"How
      How to Import Excel Data to Jupyter Notebook using Pandas<\/figcaption><\/figure>\n

      2. Unsupervised Learning<\/strong><\/h4>\n

      In case of unsupervised learning, you have a training data set made up of a set of input vectors x, without and target values. The objective of unsupervised learning is to figure out trends in a given data set. In this case,\u00a0 it is called clustering<\/em>.\u00a0 In clustering you need to find clusters or groups in the dataset that are similar.<\/p>\n

      If however, the goal is to determine how the dataset is distributed withing the input space, then it is called density estimation<\/em>.<\/p>\n

      Then it could also be to reduce data from a high-dimensional space to a lower dimensional space. In this case it is called dimensionality reduction.<\/em><\/p>\n

      Let’s now summarize what you’ve learnt so far:<\/p>\n

      Unsupervised learning is divided into three categories: clustering, density estimation and dimensionality reduction<\/p>\n

       <\/p>\n

      3. Reinforcement Learning<\/strong><\/h4>\n

      This is a relatively new area compared to the previous two. In reinforcement learning, the goal is to find a the best action to take in a specific situation\u00a0 so as to maximize some reward. In this case, no examples of optimal output is provided. The algorithm has to figure it out by a process of trial and error.<\/p>\n

      This is applied in game playing where the game have to figure out the best move to make to get it closer to winning.<\/p>\n

      However, we would not border much about this particular category. Just know that credit assignment<\/em> problems falls under this class.<\/p>\n

       <\/p>\n

      Now, the summary of all the classes of machine learning problems are given in the figure below:<\/p>\n

      \"Classes
      Classes of Machine Learning Problems – diagram<\/figcaption><\/figure>\n","protected":false},"excerpt":{"rendered":"

      This is lesson 3 of Machine Learning 101. We are going to examine classes of machine learning problems. In Lecture 2 we already mentioned a … <\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"pagelayer_contact_templates":[],"_pagelayer_content":"","footnotes":""},"categories":[16],"tags":[],"class_list":["post-1897","post","type-post","status-publish","format-standard","hentry","category-machine-learning"],"_links":{"self":[{"href":"https:\/\/kindsonthegenius.com\/blog\/wp-json\/wp\/v2\/posts\/1897","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/kindsonthegenius.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/kindsonthegenius.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/kindsonthegenius.com\/blog\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/kindsonthegenius.com\/blog\/wp-json\/wp\/v2\/comments?post=1897"}],"version-history":[{"count":1,"href":"https:\/\/kindsonthegenius.com\/blog\/wp-json\/wp\/v2\/posts\/1897\/revisions"}],"predecessor-version":[{"id":2065,"href":"https:\/\/kindsonthegenius.com\/blog\/wp-json\/wp\/v2\/posts\/1897\/revisions\/2065"}],"wp:attachment":[{"href":"https:\/\/kindsonthegenius.com\/blog\/wp-json\/wp\/v2\/media?parent=1897"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/kindsonthegenius.com\/blog\/wp-json\/wp\/v2\/categories?post=1897"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/kindsonthegenius.com\/blog\/wp-json\/wp\/v2\/tags?post=1897"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}