week 1——01Introduction

m a c h i n e   l e a r n i n g   a l g o r i t h m : machine learning algorithm: machine learning algorithm:
  - S u p e r v i s e d   l e a r n i n g = {     r e g r e s s i o n     c l a s s i f i c a t i o n     Supervised learning = \begin{cases}   regression \\   classification   \end{cases} Supervised learning={  regression  classification  
  
  - U n s u p e r v i s e d   l e a r n i n g Unsupervised learning Unsupervised learning
  
O t h e r s : R e i n f o r c e m e n t l e a r n i n g , r e c o m m e n d e r S y s t e m Others: Reinforcement learning, recommender System Others:Reinforcementlearning,recommenderSystem

Examples of unsupervised learning:

Clustering: Take a collection of 1,000,000 different genes, and find a way to automatically group these genes into groups that are somehow similar or related by different variables, such as lifespan, location, roles, and so on.

Non-clustering: The “Cocktail Party Algorithm”, allows you to find structure in a chaotic environment. (i.e. identifying individual voices and music from a mesh of sounds at a cocktail party)

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