[Machine Learning (Andrew Ng courses)] I. Introduction

1. What is machine learning?

Machine Learning is 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 task T and some performance measure P if its performance on T, as measured by P, improves with experience e.

2. Differences betweent classification and regression Problem

Classification is appropriate when we are trying to predict oneA small numberDiscrete-valued outputs, such as whether it will rain (which we might designate as Class 0), or not (say Class 1). (a small number of discrete values)

Regression is appropriate when we are trying to predict a continuous-valued output. even though the number of shares traded is technically discrete-valued (I. E ., an integer), It wocould be more natural to treat it as a continuous value (similar to the housing prices example in the lecture ). (a large number of discrete values, continuous values)

2. Differences betweent supervised learning and unsupervised learning

Supervised Learning "right answers" given. Eg.

Each data in the training set has a correct answer. The algorithm makes judgments based on the data.

Unsupervised learning: used to process unlabeled samples during classifier design.

[Machine Learning (Andrew Ng courses)] I. Introduction