Mashinali o‘qitishga kirish Nosirov Xabibullo xikmatullo o‘gli Falsafa doktori (PhD), tret kafedrasi mudiri

Unsupervised learning 

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  The only requirement to be called an unsupervised learning strategy is to learn a new feature space that captures the characteristics of the original space by maximizing some objective function or minimising some loss function.
  
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  Therefore, generating a covariance matrix is not unsupervised learning, but taking the eigenvectors of the covariance matrix is because the linear algebra eigendecomposition operation maximizes the variance; this is known as PCA.
  

Unsupervised learning 

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  Some of the most common algorithms used in unsupervised learning include:
  
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  (1) Clustering,
  
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  (2) Anomaly detection,
  
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  (3) Neural Networks, and
  
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  (4) Approaches for learning latent variable models.
  

Each approach uses several methods as follows:

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  Clustering
  
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    Hierarchical clustering,
    
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    K-means
    
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    Mixture models
    
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    Dbscan
    
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    OPTICS algorithm
    
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  Anomaly detection
  
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    Local outlier factor
    
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    Isolation forest
    
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  Neural networks
  
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    Autoencoders
    
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    Deep belief nets
    
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    Hebbian learning
    
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    Generative adversarial networks
    
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    Self-organizing map
    
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  Approaches for learning latent variable models such as
  
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    Expectation–maximization algorithm (EM)
    
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    Method of moments
    
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    Blind signal separation techniques
    
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      Principal component analysis
      
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      Independent component analysis
      
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      Non-negative matrix factorization
      
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      Singular value decomposition
      

Unsupervised learning 

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  The goal of clustering is to
  
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    group data points that are close (or similar) to each other
    
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    identify such groupings (or clusters) in an unsupervised manner
    

Unsupervised learning 

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  A cluster is represented by a single point, known as centroid (or cluster center) of the cluster
  
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  Centroid is computed as the mean of all data points in a cluster
  
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  Cluster boundary is decided by the farthest data point in the cluster
  

Application of Clustering

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  Example 1: groups people of similar sizes together to make “small”, “medium” and “large” T-Shirts.
  
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    Tailor-made for each person: too expensive
    
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    One-size-fits-all: does not fit all.
    
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  Example 2: In marketing, segment customers according to their similarities
  
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    To do targeted marketing.
    
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  Example 3: Given a collection of text documents, we want to organize them according to their content similarities,
  
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    To produce a topic hierarchy
    

Thank you! Contacts Khabibullo Nosirov, Phd Project Manager, Head Of The Department Tashkent University Of Information Technologies named after Muhammad Al-Khwarizmi Radio And Mobile Communications Faculty 100084, Amir Temur 108, Tashkent, Uzbekistan [e-mail] [phone] (WhatsApp) [phone] (Telegram) www.tuit.uz www.spacecom.uz www.intras.uz

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