diff --git a/Classification/1-Data/README.md b/Classification/1-Data/README.md index 029678ca9..1ca50ebf5 100644 --- a/Classification/1-Data/README.md +++ b/Classification/1-Data/README.md @@ -2,7 +2,8 @@ Add a sketchnote if possible/appropriate -![Embed a video here if available](video-url) +[![Introduction to Classification](https://img.youtube.com/vi/eg8DJYwdMyg/0.jpg)](https://youtu.be/eg8DJYwdMyg "Introduction to Classification") +> MIT's John Guttag introduces Classification ## [Pre-lecture quiz](link-to-quiz-app) diff --git a/Clustering/1-Visualize/README.md b/Clustering/1-Visualize/README.md index 76551df90..5cb138f71 100644 --- a/Clustering/1-Visualize/README.md +++ b/Clustering/1-Visualize/README.md @@ -8,6 +8,9 @@ Clustering is a type of [Unsupervised Learning](https://wikipedia.org/wiki/Unsup ## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/25/) ### Introduction +[![Introduction to ML](https://img.youtube.com/vi/esmzYhuFnds/0.jpg)](https://youtu.be/esmzYhuFnds "Introduction to Clustering") +> MIT's John Guttag introduces Clustering + [Clustering](https://link.springer.com/referenceworkentry/10.1007%2F978-0-387-30164-8_124) is very useful for data exploration. Let's see if it can help discover trends and patterns in the way Nigerian audiences consume music. โœ… Take a minute to think about the uses of clustering. In real life, clustering happens whenever you have a pile of laundry and need to sort out your family members' clothes ๐Ÿงฆ๐Ÿ‘•๐Ÿ‘–๐Ÿฉฒ. In data science, clustering happens when trying to analyze a user's preferences, or determine the characteristics of any unlabeled dataset. Clustering, in a way, helps make sense of chaos. diff --git a/Introduction/1-intro-to-ML/README.md b/Introduction/1-intro-to-ML/README.md index 1b979f585..53178fe28 100644 --- a/Introduction/1-intro-to-ML/README.md +++ b/Introduction/1-intro-to-ML/README.md @@ -13,7 +13,8 @@ Describe what we will learn Describe what will be covered -> Notes +[![Introduction to ML](https://img.youtube.com/vi/h0e2HAPTGF4/0.jpg)](https://youtu.be/h0e2HAPTGF4 "Introduction to ML") +> MIT's John Guttag introduces Machine Learning ### Prerequisite diff --git a/Introduction/2-history-of-ML/README.md b/Introduction/2-history-of-ML/README.md index f20f14cf7..0fa95d99a 100644 --- a/Introduction/2-history-of-ML/README.md +++ b/Introduction/2-history-of-ML/README.md @@ -1,49 +1,74 @@ -# Introduction to Machine Learning +# History of Machine Learning and AI Add a sketchnote if possible/appropriate -![Embed a video here if available](video-url) +[![The history of AI by Amy Boyd](https://img.youtube.com/vi/EJt3_bFYKss/0.jpg)](https://www.youtube.com/watch?v=EJt3_bFYKss "The history of AI by Amy Boyd") +## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/3/) -[Check out this podcast where Amy Boyd discusses the evolution of AI](http://runasradio.com/Shows/Show/739) +In this lesson, we will walk through the major milestones of the history of Machine Learning and AI. +## 1950: Machines that Think -## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/3/) +Alan Turing -Describe what we will learn +## 1956: Dartmouth Research Project -### Introduction +"To proceed on the basis of the conjecture that every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it -- John McCarthy" -Describe what will be covered +They named the field of "Artificial Intelligence". This is the first time the phrase was coined. -> Notes +## 1956 - 1974: "The Gold Rush" -### Prerequisite +Optimism was high in this era that AI could solve many problems. Research was very well funded. Shakey the robot could maneuver and decide. Eliza could converse w/ ppl. Blocksworld -What steps should have been covered before this lesson? +## 1974 - 1980: "AI Winter" -### Preparation +Funding stopped, optimism lowered. Some issues included: +compute power was too limited +combinatorial explosion: the amount of parameters needing to be trained exploded w/o compute keeping up +paucity of data, hindered the process of using algorithms +how to frame the question...were we asking the right questions, were they specific enough +lots of criticism about approaches + criticism on turing tests + chinese room theory + ethical criticism of eliza -Preparatory steps to start this lesson +scruffy vs. neat AI +neat AI has lots of trees and logical reasoning +scruffy AI encompasses an idea's metadata -led to progressions in OO programming ---- +## 1980s Expert systems -[Step through content in blocks] +knowledge became the focus of AI and its businenss impact became acknowledged -## [Topic 1] +revival of connectionism (NN) behind the scenes, in research +hopfield net +backpropagation +applied neural networks -### Task: +## 1987 - 1993: AI Chill +hardware had become too specialized +moving into an era of personal computers - computing becoming democratized -Work together to progressively enhance your codebase to build the project with shared code: +## 1990s: AI based on Robotics -```html -code blocks -``` +To show real intelligence AI needs a body -โœ… Knowledge Check - use this moment to stretch students' knowledge with open questions +## 1993 - 2011 + +Same issues start to be solved +excessive data +huge compute power +more powerful algorithms +better able to frame question -## [Topic 2] +## Now -## [Topic 3] +AI started as a single area, now there are may parts and they cross-collaborate +[![The history of Deep Learning](https://img.youtube.com/vi/mTtDfKgLm54/0.jpg)](https://www.youtube.com/watch?v=mTtDfKgLm54 "The history of Deep Learning") +> Yann LeCun discusses the history of Deep Learning in this lecture + +โœ… Knowledge Check - use this moment to stretch students' knowledge with open questions ## ๐Ÿš€Challenge Add a challenge for students to work on collaboratively in class to enhance the project @@ -54,4 +79,6 @@ Optional: add a screenshot of the completed lesson's UI if appropriate ## Review & Self Study -**Assignment**: [Assignment Name](assignment.md) +[Check out this podcast where Amy Boyd discusses the evolution of AI](http://runasradio.com/Shows/Show/739) + +**Assignment**: [Create a timeline](assignment.md)