{"id":2377,"date":"2026-01-12T15:02:21","date_gmt":"2026-01-12T22:02:21","guid":{"rendered":"https:\/\/miriamposner.com\/blog\/?p=2377"},"modified":"2026-01-12T16:42:42","modified_gmt":"2026-01-12T23:42:42","slug":"introducing-beginners-to-the-mechanics-of-machine-learning","status":"publish","type":"post","link":"https:\/\/miriamposner.com\/blog\/introducing-beginners-to-the-mechanics-of-machine-learning\/","title":{"rendered":"Introducing beginners to the mechanics of machine learning"},"content":{"rendered":"\n
\"In<\/a>
Roz learns to speak animal language. Screengrab from The Wild Robot<\/a>.<\/em><\/figcaption><\/figure>\n\n\n\n

Every year, I spend some time introducing students to the mechanics of machine learning with neural nets. I definitely don’t go into great depth; I usually only have one class for this. But I try to unpack at least some of the major concepts, so that ML isn’t quite such a black box.<\/p>\n\n\n\n

Whether you’re an AI critic or enthusiast, I find that conversations can be much more specific and productive if the participants have a basic understanding of how the tools work. That way, if students hear some kind of outlandish claim\u2014like, that ChatGPT loves them\u2014they can compare the claim to a mental image of how the tool actually works.<\/p>\n\n\n\n\n\n\n\n

For some time, I’ve been gathering tools and activities to help me do this. (Things come and go so fast on the web that I have to do it every year!) It’s always a challenge to find high-quality tools, especially since they’re buried in layers and layers of slop. So I thought people might find it useful to see the tools gathered in one place.<\/p>\n\n\n\n

These are activities and illustrations, not really readings. To read in preparation for class, I like to assign Stephen Wolfram’s “What is ChatGPT Doing…And Why Does it Work?”<\/a> (Remember, I’m just laying out the mechanics with this class, not making any particular argument about AI!)<\/p>\n\n\n\n

You can see how I put the tools together in a lecture here<\/a>. (It’s designed for use with Pear Deck, but you can easily convert it to a regular Google Slides lecture.)<\/p>\n\n\n\n

    \n
  1. A 1986 AT&T Bell Labs video on expert systems<\/a> because I think it’s useful for students to compare ML with other AI approaches. (In this list, videos are linked to the timestamp indicating when I begin the video in lecture)<\/li>\n\n\n\n
  2. A moment in The Wild Robot<\/em><\/a> when Roz uses ML to learn to speak to animals<\/li>\n\n\n\n
  3. A simple but helpful video<\/a> on ML from Oxford<\/li>\n\n\n\n
  4. A very simple animation <\/a>of how LLMs predict the next word in a sequence (sorry, I know there’s a way to get the video out of Reddit, but I can’t figure it out)<\/li>\n\n\n\n
  5. An interactive Washington Post<\/em> article<\/a> that helps students understand the composition of one LLM corpus<\/li>\n\n\n\n
  6. What’s In My Big Data?<\/a>, a more detailed analysis and comparison of several corpora<\/li>\n\n\n\n
  7. “But What Is a Neural Network,” f<\/a>rom the great 3Blue1Brown–I start at the linked timestamp and go for a few minutes, until he threatens to get into the math<\/li>\n\n\n\n
  8. A series of animations<\/a> (see this series, too<\/a>) exploring different parts of LLMs. In class, I use 5 (“Generative AI: Artificial Neuron”)<\/a>, 10 (“Generative AI: Neuron Sequence”)<\/a>, and 24 (“Identifying Animals with AI”)<\/a>. The last one is a particular favorite. You do have to create an account to access some of these animations, and students won’t be able to access them easily, so they’re more of a demo than a really interactive activity.<\/li>\n\n\n\n
  9. Google’s Teachable Machine<\/a>, which allows you to train your own model. We train it in class to distinguish between students’ pens and water bottles. Sounds unwieldy, but it only takes about 10 minutes!<\/li>\n\n\n\n
  10. My favorite simple, clear illustration<\/a> of back-propagation. (You can refer them to this video<\/a> if they want more detail.)<\/li>\n\n\n\n
  11. “Exploring Neural Networks with Activation Atlases,”<\/a> which attempts to help people understand what happens in networks’ hidden layers (still really confusing, TBH)<\/li>\n\n\n\n
  12. Quick illustration<\/a> of some methods of fine-tuning LLMs<\/li>\n\n\n\n
  13. Quick illustration<\/a> of RAG<\/li>\n\n\n\n
  14. (Pretty advanced and detailed) LLM visualization<\/a><\/li>\n\n\n\n
  15. Neural Network Playground<\/a><\/li>\n\n\n\n
  16. Some great hands-on activity ideas<\/a> (it says \u201cmiddle school,” but I think they\u2019re still useful!)<\/li>\n\n\n\n
  17. metaLAB\u2019s AI Pedagogy Project<\/a><\/li>\n\n\n\n
  18. A People\u2019s Guide to Finding Algorithmic Bias<\/a><\/li>\n\n\n\n
  19. Some great hands-on lesson ideas<\/a> (I like \u201cBrain-in-a-Bag<\/a>\u201d)<\/li>\n\n\n\n
  20. A Colab notebook by Ulysses Pascal<\/a> (a former TA) that allows students to tweak GPT<\/li>\n<\/ol>\n","protected":false},"excerpt":{"rendered":"

    Every year, I spend some time introducing students to the mechanics of machine learning with neural nets. I definitely don’t go into great depth; I usually only have one class for this. But I try to unpack at least some of the major concepts, so that ML isn’t quite such a black box. Whether you’re […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[],"class_list":["post-2377","post","type-post","status-publish","format-standard","hentry","category-uncategorized"],"_links":{"self":[{"href":"https:\/\/miriamposner.com\/blog\/wp-json\/wp\/v2\/posts\/2377","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/miriamposner.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/miriamposner.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/miriamposner.com\/blog\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/miriamposner.com\/blog\/wp-json\/wp\/v2\/comments?post=2377"}],"version-history":[{"count":3,"href":"https:\/\/miriamposner.com\/blog\/wp-json\/wp\/v2\/posts\/2377\/revisions"}],"predecessor-version":[{"id":2382,"href":"https:\/\/miriamposner.com\/blog\/wp-json\/wp\/v2\/posts\/2377\/revisions\/2382"}],"wp:attachment":[{"href":"https:\/\/miriamposner.com\/blog\/wp-json\/wp\/v2\/media?parent=2377"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/miriamposner.com\/blog\/wp-json\/wp\/v2\/categories?post=2377"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/miriamposner.com\/blog\/wp-json\/wp\/v2\/tags?post=2377"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}