Andrew Ng Mechine Learning notes

这是学习吴恩达《机器学习》的相关笔记

相关内容:深度学习计划

What is machine learning

There isn’t a well accepted definition of what is and what isn’t machine learning.

Suppose your email program watches which emails you do or do not mark as spam, and based on that learns how to better filter spam. What is the task T in this setting?

A. Classifying emails as spam or not spam. ✅️ T

B. Watching you label emails as spam or not spam. E

C. The number (or fraction) of emails correctly classified as spam/not spam. P

D. None of the above—this is not a machine learning problem

Supervised Leaening

“right answers” given --> more right answers

Regression: Predict continuous valued output

Classification: Prodict a discrete valued output

Problem 1: You have a large inventory of identical items. You want to predict how many of these items will sell over the next 3 months.

Problem 2: You’d like software to examine individual customer accounts, and for each account decide if it has been hacked/compromised.

Problem 1 is a regression problem, Problem 2 is a Classification problem.

Unspervised Learning

The unspervised learning algorithm may break these data into these two separate clusters. This is called clustring algorithm.(聚类算法)

Cooktail part problem algorithm

Problem environment

Octave, open source software, many learning algotithms become just a few lines of code to implement.

Of the following examples, which would you address using an unsupervised learning algorithm? (Check all that apply.)

Given email labeled as spam/not spam, learn a spam filter.

Given a set of news articles found on the web, group them into set of articles about the same story. ✅️

Given a database of customer data, automatically discover market segments and group customers into different market segments. ✅️

Given a dataset of patients diagnosed as either having diabetes or not, learn to classify new patients as having diabetes or not.