Start Date: 01/24/2021
Course Type: Common Course |
Course Link: https://www.coursera.org/learn/ibm-unsupervised-learning
This course introduces you to one of the main types of Machine Learning: Unsupervised Learning. You will learn how to find insights from data sets that do not have a target or labeled variable. You will learn several clustering and dimension reduction algorithms for unsupervised learning as well as how to select the algorithm that best suits your data. The hands-on section of this course focuses on using best practices for unsupervised learning.
Introduction to Unsupervised Learning and K Means
Selecting a clustering algorithm
Dimensionality Reduction
This course introduces you to one of the main types of Machine Learning: Unsupervised Learning. You will learn how to fi
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Unsupervised learning | A central case of unsupervised learning is the problem of density estimation in statistics, though unsupervised learning encompasses many other problems (and solutions) involving summarizing and explaining key features of the data. |
Unsupervised learning | 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. |
Unsupervised learning | 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. |
Unsupervised 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). |
Unsupervised learning | 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. |
Machine learning | Clustering is a method of unsupervised learning, and a common technique for statistical data analysis. |
Ensemble learning | and unsupervised learning (density estimation). It has also been used to |
Unsupervised learning | 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." 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. |
Unsupervised learning | In particular, the method of moments is shown to be effective in learning the parameters of latent variable models. |
Unsupervised learning | 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. |
Fusion adaptive resonance theory | paradigms, including unsupervised learning, supervised learning, as well as reinforcement learning. |
Ensemble learning | By analogy, ensemble techniques have been used also in unsupervised learning scenarios, for example in consensus clustering or in anomaly detection. |
Unsupervised learning | 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. |
Helmholtz machine | Helmholtz machines are usually trained using an unsupervised learning algorithm, such as the wake-sleep algorithm. |
Natural computing | Particle swarm optimization algorithms have been applied to various optimization problems, and to unsupervised learning, game learning, and scheduling applications. |
Artificial neural network | There are three major learning paradigms, each corresponding to a particular abstract learning task. These are supervised learning, unsupervised learning and reinforcement learning. |
Deep learning | Many deep learning algorithms are applied to unsupervised learning tasks. This is an important benefit because unlabeled data are usually more abundant than labeled data. Examples of deep structures that can be trained in an unsupervised manner are neural history compressors and deep belief networks. |
Feature learning | Local linear embedding (LLE) is a nonlinear unsupervised learning approach for generating low-dimensional neighbor-preserving representations from (unlabeled) high-dimension input. The approach was proposed by Sam T. Roweis and Lawrence K. Saul in 2000. |
K-means clustering | "k"-means clustering has been used as a feature learning (or dictionary learning) step, in either (semi-)supervised learning or unsupervised learning. |
Autoencoder | An autoencoder, autoassociator or Diabolo network is an artificial neural network used for unsupervised learning of efficient codings. |