Machine Learning
What is Machine Learning:
- Define a set of funtion
- goodness of function
- pick the best function
About Machine Learning
Supervised Learning
Regression&classification ∈ Supervised Learning(Learning form teacher).
Supervised Learning needs Training Data.
Supervised Learning is a defined Answer teach.
Supervised Learning needs Input/Output,Pairs of target,function/Output=Label.
Regression
The output of the target function f is scalar.
Classification
Binary Classification
Function f -> Yes/No(Only two answer).
e.g.spam.
Multi-Class Classification
Function f -> class1/class2/class3/……(many classes to choose).
e.g.news on google to classify.
Linear Model
Non-Linear Model
e.g.Deep Learning:Image Recognition,Play go.
SVM.
Decision Tree.
K-NM.
Structured Learning
Beyond Classification.
Output a complex event.
e.g.Face Recognition.
Semi-supervised Learning
Decrease the amount of data.
Only have Labeled and Unlabeled.
Transfer Learning
Labeled&Unlabeled&No Related Labeled&No Related Unlabeled.
Unsupervised Learning
Unlabeled or words learning.
e.g.Machine reading:Machine learns the meaning of words from reading a lot of docs.
Machine drawing：only inputs.
Reinforcement Learning
Learning form critics.
Machine only has a score.