After writing yesterday's post, I was thinking about how much students should know about algorithms if they are to have a basic understanding of how AI works. Is it enough to tell them an algorithm is a set of instructions? So I turned, as I often do, to Khan Academy -- a free online learning site that often helps me through my lack of a mathematics background. I found a set of three short lessons, starting with a video. In the introductory video, "What is an algorithm and why should you care?", we see various practical uses of algorithms, followed by the statement above, and a brief description of how route finding works -- what Google Maps does when it gives you directions.

Following on from yesterday's post, today I looked at more lessons in Introduction to Machine Learning from the Google News Initiative. The separation of machine learning into three different approaches -- supervised learning, unsupervised learning, and reinforcement learning -- is standard (Lesson 3). In keeping with the course's focus on journalism applications of ML, the example given for supervised learning is The Atlanta Journal-Constitution's deservedly famous investigative story about sex abuse of patients by doctors. Supervised learning was used to sort more than 100,000 disciplinary reports on doctors. The example of unsupervised learning is one I hadn't seen before.

Two days ago, I came upon this newly published course from FastAI: Practical Deep Learning for Coders. I actually stumbled across it via a video on YouTube, which I've watched now, and it made me feel optimistic about the course. I'm in the middle of the CS50 AI course from Harvard, though, so I need to hold off on the FastAI course for now. The first video got me thinking. First, they said (as many others have said) that Python is the main programming language used for machine learning today.

Supervised learning is a type of machine learning in which a model is trained using labeled data. You begin with a very large collection of labeled data. For the Iris Data Set, the data all refer to individual iris flowers, which can be divided into three related species. For the MNIST dataset, the data are images of about 70,000 handwritten numbers, 0 through 9.) You divide the dataset into two parts, the training data and the test data. The split might be 30/70, or 40/60.