Introduction to Machine Learning

The demonstrations in this series use the Python programming language, the most popular and widely supported language in machine learning. Dr. Littman shows you how to run programming examples from your web browser, which avoids the need to install the software on your own computer, saving installation headaches and giving you more processing power than is available on a typical home computer.

Can machine learning beat a rhyming rule, taught in elementary school, for determining whether a word is spelled with an I-E or an E-I-as in "diet" and "weigh"? Discover that a decision tree is a convenient tool for approaching this problem. After experimenting, use Python to build a decision tree for predicting the likelihood for an individual to develop diabetes based on eight health factors.

Graduate to a more difficult class of problems: learning from images and auditory information. Here, it makes sense to address the task more or less the way the brain does, using a form of computation called a neural network. Explore the general characteristics of this powerful tool. Among the examples, compare decision-tree and neural-network approaches to recognizing handwritten digits.

Take a deeper dive into neural networks by working through a simple algorithm implemented in Python. Return to the green-screen problem from the first episode to build a learning algorithm that places the professor against a new backdrop.

A program need not understand the content of an email to know with high probability that it's spam. Discover how machine learning does so with the Naive Bayes approach, which is a simplified application of Bayes' theorem to a simplified model of language generation. The technique illustrates a very useful strategy: going backward from effects (in this case, words) to their causes (spam).

When you encounter a new type of problem and don't yet know the best machine learning strategy to solve it, a ready first approach is a genetic algorithm. These programs apply the principles of evolution to artificial intelligence, employing natural selection over many generations to optimize your results. Analyze several examples, including finding where to aim.

Simple to use and speedy to execute, the nearest neighbor algorithm works on the principle that adjacent elements in a dataset are likely to share similar characteristics. Try out this strategy for determining a comfortable combination of temperature and humidity in a house. Then, dive into the problem of malware detection, seeing how the nearest neighbor rule can sort good software from bad.

Having covered the five fundamental classes of machine learning in the previous episodes, now focus on a risk common to all: overfitting. This is the tendency to model training data too well, which can harm the performance on the test data. Practice avoiding this problem using the diabetes dataset from episode 3. Hear tips on telling the difference between real signals and spurious associations.

Explore pitfalls that loom when applying machine learning algorithms to real-life problems. For example, see how survival statistics from a boating disaster can lead to false conclusions. Also, look at cases from medical care and law enforcement that reveal hidden biases in the way data is interpreted. Since an algorithm is doing the interpreting, understanding what's happening can be a challenge.