Machine Learning – Andrew Ng Coursera

The field of study that gives computers the ability to learn without being explicitly programmed

A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E

Supervised Learning and Unsupervised Learning

Give the algorithm data set in which the "right answers" are provided The job of the algorithm is to produce more "right answers". Supervised learning is the machine learning task of inferring a function from labeled training data.

The training data consist of a set of training examples. In supervised learning, each example is a pair consisting of an input object (typically a vector) and a desired output value (also called the supervisory signal).

Supervised learning problems are categorized into "regression" and "classification" problems

Regression problems try to predict continuous value output (often fitting data to a line)

Classification problems try to predict a discrete value output: is it or isn't it what we are looking for (often setting an output to either zero or one)

An algorithm that can deal with infinite number of features

Unsupervised learning is the task of making an inference from data without the "correct answers" given (unlabeled data). Its a learning setting where you give the algorithm a ton of data and just ask it to find structure in the data for us.

Anomaly Detection Clustering Dimensionality Reduction

Clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters).

Algorithms that allow you to find structure in a chaotic environment. (i.e.

identifying individual voices and music from a mesh of sounds at a cocktail party).