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(ML 1.3) What is unsupervised learning?
(ML 1.3) What is unsupervised learning?
Published: 2011/06/09
Channel: mathematicalmonk
Unsupervised Learning - Georgia Tech - Machine Learning
Unsupervised Learning - Georgia Tech - Machine Learning
Published: 2015/02/23
Channel: Udacity
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Unsupervised Learning explained
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Channel: deeplizard crypto & ai
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 - 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
Supervised & Unsupervised Learning
Supervised & Unsupervised Learning
Published: 2015/11/29
Channel: Analytics University
Machine Learning - Supervised VS Unsupervised Learning
Machine Learning - Supervised VS Unsupervised Learning
Published: 2017/03/14
Channel: Cognitive Class
Deep Learning with Tensorflow - Introduction to Unsupervised Learning
Deep Learning with Tensorflow - Introduction to Unsupervised Learning
Published: 2017/02/01
Channel: Cognitive Class
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
Unsupervised Learning - Georgia Tech - Machine Learning
Unsupervised Learning - Georgia Tech - Machine Learning
Published: 2014/04/14
Channel: Udacity
UnSupervised Learning by Andrew Ng
UnSupervised Learning by Andrew Ng
Published: 2016/01/26
Channel: Han Yu
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
Unsupervised Learning: Introduction to K-mean Clustering
Unsupervised Learning: Introduction to K-mean Clustering
Published: 2017/12/07
Channel: Shokoufeh Mirzaei
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
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
Unsupervised Learning
Unsupervised Learning
Published: 2016/06/06
Channel: Udacity
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
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
Machine Learning #03 What is Unsupervised Learning ?
Machine Learning #03 What is Unsupervised Learning ?
Published: 2017/10/21
Channel: Xoviabcs
1.1.3 Machine Learning Introduction - Unsupervised Learning
1.1.3 Machine Learning Introduction - Unsupervised Learning
Published: 2016/04/06
Channel: Manohar Mukku
Mod-01 Lec-34 Unsupervised Learning - Clustering
Mod-01 Lec-34 Unsupervised Learning - Clustering
Published: 2014/06/02
Channel: nptelhrd
Lecture 18: ANN - Unsupervised Learning - 1  Competitive Learning
Lecture 18: ANN - Unsupervised Learning - 1 Competitive Learning
Published: 2015/11/08
Channel: iugaza1
Unsupervised Learning
Unsupervised Learning
Published: 2018/01/19
Channel: Simons Institute
Clustering Introduction - Practical Machine Learning Tutorial with Python p.34
Clustering Introduction - Practical Machine Learning Tutorial with Python p.34
Published: 2016/06/07
Channel: sentdex
CS231n Lecture 14 - Videos and Unsupervised Learning
CS231n Lecture 14 - Videos and Unsupervised Learning
Published: 2016/06/14
Channel: MachineLearner
K-Means Clustering - The Math of Intelligence (Week 3)
K-Means Clustering - The Math of Intelligence (Week 3)
Published: 2017/07/05
Channel: Siraj Raval
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
Machine Learning in R - Supervised vs. Unsupervised
Machine Learning in R - Supervised vs. Unsupervised
Published: 2015/12/05
Channel: DataCamp
Machine Learning - Unsupervised Learning
Machine Learning - Unsupervised Learning
Published: 2017/03/18
Channel: Cognitive Class
Unsupervised Algorithms in Machine Learning
Unsupervised Algorithms in Machine Learning
Published: 2014/09/21
Channel: Analytics University
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
#2.2. 머신러닝의 종류 - Unsupervised Learning
#2.2. 머신러닝의 종류 - Unsupervised Learning
Published: 2017/01/10
Channel: Terry TaeWoong Um
Foundations of Unsupervised Deep Learning TensorFlow - Ruslan Salakhutdinov, CMU
Foundations of Unsupervised Deep Learning TensorFlow - Ruslan Salakhutdinov, CMU
Published: 2017/06/08
Channel: Artificial Intelligence - Project Godbrain
1. Unsupervised Learning Introduction
1. Unsupervised Learning Introduction
Published: 2013/11/02
Channel: Artificial Intelligence Courses
4. Unsupervised Learning
4. Unsupervised Learning
Published: 2013/10/31
Channel: Artificial Intelligence Courses
ML Lecture 13: Unsupervised Learning - Linear Methods
ML Lecture 13: Unsupervised Learning - Linear Methods
Published: 2016/11/18
Channel: 李宏毅
Unsupervised learning of skeletons from motion
Unsupervised learning of skeletons from motion
Published: 2008/12/12
Channel: David Ross
Office Hours - Topic Modeling: Unsupervised Learning in Text Analysis
Office Hours - Topic Modeling: Unsupervised Learning in Text Analysis
Published: 2015/09/18
Channel: Springboard
Yann LeCun Lecture 8/8 Unsupervised Learning
Yann LeCun Lecture 8/8 Unsupervised Learning
Published: 2016/06/06
Channel: trwappers
Artificial Intelligence Tutorial #17: The Unsupervised Learning (Clustering)
Artificial Intelligence Tutorial #17: The Unsupervised Learning (Clustering)
Published: 2016/12/13
Channel: Ranji Raj
Artificial Neural Networks - Unsupervised learning
Artificial Neural Networks - Unsupervised learning
Published: 2015/10/16
Channel: Iberius Pred
Unsupervised Learning of Spoken Language with Visual Context
Unsupervised Learning of Spoken Language with Visual Context
Published: 2017/02/10
Channel: Center for Brains, Minds and Machines (CBMM)
Unit 6 8 Supervised vs Unsupervised Learning
Unit 6 8 Supervised vs Unsupervised Learning
Published: 2011/10/23
Channel: knowitvideos
Machine Learning - Unsupervised Learning - Density Based Clustering
Machine Learning - Unsupervised Learning - Density Based Clustering
Published: 2017/04/20
Channel: Cognitive Class
Supervised Learning - Georgia Tech - Machine Learning
Supervised Learning - Georgia Tech - Machine Learning
Published: 2015/02/23
Channel: Udacity
Learning From Simulated and Unsupervised Images Through Adversarial Training
Learning From Simulated and Unsupervised Images Through Adversarial Training
Published: 2017/07/25
Channel: ComputerVisionFoundation Videos
NIPS 2016 Spotlight - Unsupervised Learning for Physical Interaction through Video Prediction
NIPS 2016 Spotlight - Unsupervised Learning for Physical Interaction through Video Prediction
Published: 2016/11/14
Channel: CHELSEA FINN
What is Unsupervised Learning? Netflix User Recommendations using Artificial Intelligence
What is Unsupervised Learning? Netflix User Recommendations using Artificial Intelligence
Published: 2017/06/13
Channel: Oscar Alsing
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
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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[edit]

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 in 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. In contrast, 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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