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(ML 1.3) What is unsupervised learning?
(ML 1.3) What is unsupervised learning?
Published: 2011/06/09
Channel: mathematicalmonk
RI Seminar: Yann LeCun : The Next Frontier in AI: Unsupervised Learning
RI Seminar: Yann LeCun : The Next Frontier in AI: Unsupervised Learning
Published: 2016/11/19
Channel: cmurobotics
Unsupervised Learning - Georgia Tech - Machine Learning
Unsupervised Learning - Georgia Tech - Machine Learning
Published: 2015/02/23
Channel: Udacity
Unsupervised Learning - Intro to Machine Learning
Unsupervised Learning - Intro to Machine Learning
Published: 2015/02/23
Channel: Udacity
Foundations of Unsupervised Deep Learning (Ruslan Salakhutdinov, CMU)
Foundations of Unsupervised Deep Learning (Ruslan Salakhutdinov, CMU)
Published: 2016/09/27
Channel: Lex Fridman
Deep Learning with Tensorflow - Introduction to Unsupervised Learning
Deep Learning with Tensorflow - Introduction to Unsupervised Learning
Published: 2017/02/01
Channel: Cognitive Class
Supervised & Unsupervised Learning
Supervised & Unsupervised Learning
Published: 2015/11/29
Channel: Analytics University
Unsupervised Learning - Georgia Tech - Machine Learning
Unsupervised Learning - Georgia Tech - Machine Learning
Published: 2014/04/14
Channel: Udacity
Andrew Ng: Deep Learning, Self-Taught Learning and Unsupervised Feature Learning
Andrew Ng: Deep Learning, Self-Taught Learning and Unsupervised Feature Learning
Published: 2013/05/14
Channel: 黄鑫
Machine Learning - Unsupervised Learning 1:42
Machine Learning - Unsupervised Learning 1:42
Published: 2017/03/18
Channel: Cognitive Class
Unsupervised Learning
Unsupervised Learning
Published: 2016/06/06
Channel: Udacity
Unsupervised Representation Learning
Unsupervised Representation Learning
Published: 2017/03/30
Channel: Simons Institute
Unsupervised Algorithms in Machine Learning
Unsupervised Algorithms in Machine Learning
Published: 2014/09/21
Channel: Analytics University
CS231n Lecture 14 - Videos and Unsupervised Learning
CS231n Lecture 14 - Videos and Unsupervised Learning
Published: 2016/06/14
Channel: MachineLearner
Unsupervised Machine Learning - Hierarchical Clustering with Mean Shift Scikit-learn and Python
Unsupervised Machine Learning - Hierarchical Clustering with Mean Shift Scikit-learn and Python
Published: 2015/02/02
Channel: sentdex
Unsupervised learning of skeletons from motion
Unsupervised learning of skeletons from motion
Published: 2008/12/12
Channel: David Ross
Data Science and (Unsupervised) Machine Learning with scikit-learn
Data Science and (Unsupervised) Machine Learning with scikit-learn
Published: 2014/12/02
Channel: mldb.ai
Machine Learning in R - Supervised vs. Unsupervised
Machine Learning in R - Supervised vs. Unsupervised
Published: 2015/12/05
Channel: DataCamp
Machine Learning - Supervised VS Unsupervised Learning 5:04
Machine Learning - Supervised VS Unsupervised Learning 5:04
Published: 2017/03/14
Channel: Cognitive Class
Facebook AI Director: The Next Frontier in AI, Unsupervised Learning
Facebook AI Director: The Next Frontier in AI, Unsupervised Learning
Published: 2017/04/28
Channel: Artificial Intelligence A.I.
Neural networks [7.3] : Deep learning - unsupervised pre-training
Neural networks [7.3] : Deep learning - unsupervised pre-training
Published: 2013/11/16
Channel: Hugo Larochelle
1. Unsupervised Learning Introduction
1. Unsupervised Learning Introduction
Published: 2013/11/02
Channel: Artificial Intelligence Courses
Machine Learning - Unsupervised Learning - Density Based Clustering 4:49
Machine Learning - Unsupervised Learning - Density Based Clustering 4:49
Published: 2017/04/20
Channel: Cognitive Class
MLAI 2015: Lecture 7 Unsupervised Learning
MLAI 2015: Lecture 7 Unsupervised Learning
Published: 2015/11/17
Channel: Open Data Science Initiative
UnSupervised Learning by Andrew Ng
UnSupervised Learning by Andrew Ng
Published: 2016/01/26
Channel: Han Yu
Mod-01 Lec-34 Unsupervised Learning - Clustering
Mod-01 Lec-34 Unsupervised Learning - Clustering
Published: 2014/06/02
Channel: nptelhrd
ML E5- What is Machine Learning? Supervised Vs Unsupervised Learning!
ML E5- What is Machine Learning? Supervised Vs Unsupervised Learning!
Published: 2016/08/28
Channel: Infinity Learn
How supervised and unsupervised classification algorithms work
How supervised and unsupervised classification algorithms work
Published: 2014/10/23
Channel: Thales Sehn Körting
Lecture 13.1 —  Clustering | Unsupervised Learning | Introduction — [ Andrew Ng ]
Lecture 13.1 — Clustering | Unsupervised Learning | Introduction — [ Andrew Ng ]
Published: 2017/02/10
Channel: Video Tutorials - All in One
Data Wrangling with DSS: From Scraping HTML To Unsupervised Learning in 1h
Data Wrangling with DSS: From Scraping HTML To Unsupervised Learning in 1h
Published: 2017/02/10
Channel: NYC Data Science Academy
Build an Autoencoder in 5 Min - Fresh Machine Learning #5
Build an Autoencoder in 5 Min - Fresh Machine Learning #5
Published: 2016/07/31
Channel: Siraj Raval
Adam Coates -- Demystifying Unsupervised Feature Learning -- UC Berkeley 12/7/2012
Adam Coates -- Demystifying Unsupervised Feature Learning -- UC Berkeley 12/7/2012
Published: 2012/12/16
Channel: Pieter Abbeel
Machine Learning - Unsupervised Learning K Means Clustering Advantages & Disadvantages 5:06
Machine Learning - Unsupervised Learning K Means Clustering Advantages & Disadvantages 5:06
Published: 2017/04/10
Channel: Cognitive Class
A Non-generative Framework and Convex Relaxations for Unsupervised Learning
A Non-generative Framework and Convex Relaxations for Unsupervised Learning
Published: 2017/03/27
Channel: Simons Institute
1.1.3 Machine Learning Introduction - Unsupervised Learning
1.1.3 Machine Learning Introduction - Unsupervised Learning
Published: 2016/04/06
Channel: Manohar Mukku
CS231n Winter 2016: Lecture 14: Videos and Unsupervised Learning
CS231n Winter 2016: Lecture 14: Videos and Unsupervised Learning
Published: 2016/03/01
Channel: Andrej Karpathy
Artificial Neural Networks - Unsupervised learning
Artificial Neural Networks - Unsupervised learning
Published: 2015/10/16
Channel: Iberius Pred
Machine Learning - Unsupervised Learning - Measuring the Distances Between Clusters 2:13
Machine Learning - Unsupervised Learning - Measuring the Distances Between Clusters 2:13
Published: 2017/04/16
Channel: Cognitive Class
ML Lecture 17: Unsupervised Learning - Deep Generative Model (Part I)
ML Lecture 17: Unsupervised Learning - Deep Generative Model (Part I)
Published: 2016/11/27
Channel: 李宏毅
Office Hours - Topic Modeling: Unsupervised Learning in Text Analysis
Office Hours - Topic Modeling: Unsupervised Learning in Text Analysis
Published: 2015/09/18
Channel: Springboard
Lecture 1.3 — Introduction Unsupervised Learning — [ Machine Learning | Andrew Ng]
Lecture 1.3 — Introduction Unsupervised Learning — [ Machine Learning | Andrew Ng]
Published: 2016/12/06
Channel: Video Tutorials - All in One
Machine learning W1 04   Unsupervised Learning
Machine learning W1 04 Unsupervised Learning
Published: 2014/12/15
Channel: Alireza Saberi
Unsupervised Learning of Multi-Hypothesized Pick-and-Place Task Templates via Crowdsourcing
Unsupervised Learning of Multi-Hypothesized Pick-and-Place Task Templates via Crowdsourcing
Published: 2015/03/25
Channel: WPI RAIL
Tensor methods for largescale unsupervised learning applications to topic and community modeling
Tensor methods for largescale unsupervised learning applications to topic and community modeling
Published: 2015/03/14
Channel: Anima Anandkumar
Lecture 22: Unsupervised Learning on Graphs
Lecture 22: Unsupervised Learning on Graphs
Published: 2016/11/18
Channel: Machine Learning CMU 10-605 Fall 2016
Machine Learning Tutorial   4   Unsupervised Learning
Machine Learning Tutorial 4 Unsupervised Learning
Published: 2016/07/23
Channel: Braintemple Tutorial TV
Machine Learning Made Easy
Machine Learning Made Easy
Published: 2015/09/18
Channel: MATLAB
Building Machine Learn Sys with TensorFlow : Learn from Data –Unsupervised Learning | packtpub.com
Building Machine Learn Sys with TensorFlow : Learn from Data –Unsupervised Learning | packtpub.com
Published: 2017/04/10
Channel: Packt Video
Data Analysis:  Clustering and Classification (Lec. 1, part 1)
Data Analysis: Clustering and Classification (Lec. 1, part 1)
Published: 2016/02/20
Channel: Nathan Kutz
Unsupervised Learning of a Scene-Specific Coarse Gaze Estimator
Unsupervised Learning of a Scene-Specific Coarse Gaze Estimator
Published: 2013/09/02
Channel: ActiveVision Oxford
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WIKIPEDIA ARTICLE

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Unsupervised machine learning is the machine learning task of inferring a function to describe hidden structure from "unlabeled" data (a classification or categorization is not included in the observations). Since the examples given to the learner are unlabeled, there is no evaluation of the accuracy of the structure that is output by the relevant algorithm—which is one way of distinguishing unsupervised learning from supervised learning and reinforcement learning.

A central case of unsupervised learning is the problem of density estimation in statistics,[1] though unsupervised learning encompasses many other problems (and solutions) involving summarizing and explaining key features of the data.

Approaches to unsupervised learning include:

In neural networks[edit]

The classical example of unsupervised learning in the study of both natural and artificial neural networks is subsumed by Donald Hebb's principle, that is, neurons that fire together wire together. In Hebbian learning, the connection is reinforced irrespective of an error, but is exclusively a function of the coincidence between action potentials between the two neurons. A similar version that modifies synaptic weights takes into account the time between the action potentials (spike-timing-dependent plasticity or STDP). Hebbian Learning has been hypothesized to underlie a range of cognitive functions, such as pattern recognition and experiential learning.

Among neural network models, the self-organizing map (SOM) and adaptive resonance theory (ART) are commonly used unsupervised learning algorithms. The SOM is a topographic organization in which nearby locations in the map represent inputs with similar properties. The ART model allows the number of clusters to vary with problem size and lets the user control the degree of similarity between members of the same clusters by means of a user-defined constant called the vigilance parameter. ART networks are also used for many pattern recognition tasks, such as automatic target recognition and seismic signal processing. The first version of ART was "ART1", developed by Carpenter and Grossberg (1988).[4]

Method of moments[edit]

One of the statistical approaches for unsupervised learning is the method of moments. In the method of moments, the unknown parameters (of interest) in the model are related to the moments of one or more random variables, and thus, these unknown parameters can be estimated given the moments. The moments are usually estimated from samples empirically. The basic moments are first and second order moments. For a random vector, the first order moment is the mean vector, and the second order moment is the covariance matrix (when the mean is zero). Higher order moments are usually represented using tensors which are the generalization of matrices to higher orders as multi-dimensional arrays.

In particular, the method of moments is shown to be effective in learning the parameters of latent variable models.[5] Latent variable models are statistical models where in addition to the observed variables, a set of latent variables also exists which is not observed. A highly practical example of latent variable models in machine learning is the topic modeling which is a statistical model for generating the words (observed variables) in the document based on the topic (latent variable) of the document. In the topic modeling, the words in the document are generated according to different statistical parameters when the topic of the document is changed. It is shown that method of moments (tensor decomposition techniques) consistently recover the parameters of a large class of latent variable models under some assumptions.[5]

The Expectation–maximization algorithm (EM) is also one of the most practical methods for learning latent variable models. However, it can get stuck in local optima, and it is not guaranteed that the algorithm will converge to the true unknown parameters of the model. Alternatively, for the method of moments, the global convergence is guaranteed under some conditions.[5]

Examples[edit]

Behavioral-based detection in network security has become a good application area for a combination of supervised- and unsupervised-machine learning. This is because the amount of data for a human security analyst to analyze is impossible (measured in terabytes per day) to review to find patterns and anomalies. According to Giora Engel, co-founder of LightCyber, in a Dark Reading article, "The great promise machine learning holds for the security industry is its ability to detect advanced and unknown attacks -- particularly those leading to data breaches."[6] The basic premise is that a motivated attacker will find their way into a network (generally by compromising a user's computer or network account through phishing, social engineering or malware). The security challenge then becomes finding the attacker by their operational activities, which include reconnaissance, lateral movement, command & control and exfiltration. These activities--especially reconnaissance and lateral movement--stand in contrast to an established baseline of "normal" or "good" activity for each user and device on the network. The role of machine learning is to create ongoing profiles for users and devices and then find meaningful anomalies.[7]

See also[edit]

Notes[edit]

  1. ^ Jordan, Michael I.; Bishop, Christopher M. (2004). "Neural Networks". In Allen B. Tucker. Computer Science Handbook, Second Edition (Section VII: Intelligent Systems). Boca Raton, FL: Chapman & Hall/CRC Press LLC. ISBN 1-58488-360-X. 
  2. ^ Hastie,Trevor,Robert Tibshirani, Friedman,Jerome (2009). The Elements of Statistical Learning: Data mining,Inference,and Prediction. New York: Springer. pp. 485–586. ISBN 978-0-387-84857-0. 
  3. ^ Acharyya, Ranjan (2008); A New Approach for Blind Source Separation of Convolutive Sources, ISBN 978-3-639-07797-1 (this book focuses on unsupervised learning with Blind Source Separation)
  4. ^ Carpenter, G.A. & Grossberg, S. (1988). "The ART of adaptive pattern recognition by a self-organizing neural network" (PDF). Computer. 21: 77–88. doi:10.1109/2.33. 
  5. ^ a b c Anandkumar, Animashree; Ge, Rong; Hsu, Daniel; Kakade, Sham; Telgarsky, Matus (2014). "Tensor Decompositions for Learning Latent Variable Models" (PDF). Journal of Machine Learning Research (JMLR). 15: 2773–2832. 
  6. ^ Engel, Giora (February 11, 2016). "3 Flavors of Machine Learning: Who, What & Where". Dark Reading. Retrieved 2016-11-21. 
  7. ^ "The R&D Pipeline Continues: Launching Version 11.1—Stephen Wolfram". blog.stephenwolfram.com. Retrieved 2017-03-22. 

Further reading[edit]

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