Andrew Ng Mechine Learning notes
What is machine learning
There isn’t a well accepted definition of what is and what isn’t machine learning.
- Arthur Samuel (1959). Machine Learning: Filed of study that gives computers the ability to learn without being explicitly programmed. （在没有明确设置的情况下使计算机具有学习能力的研究领域）
- TomMitchell (1998) Well-posedLearning Problem: 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. （一个适当的学习问题定义如下，计算机程序从经验E中学习，解决某一任务T，进行某一性能度量P，通过P测定在T上的表现因E而提高）
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
“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.
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
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.