Unsupervised Learning in Recommendation Systems

Apr 23, 2025

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Explore the role of unsupervised learning in recommendation systems and learn how to build them with expert guidance from the best machine learning training in Delhi. Ideal for anyone pursuing a machine learning course in Delhi or seeking certification.<br>

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Unsupervised Learning in Recommendation Systems

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Unsupervised Learning: Clustering

Unsupervised Learning: Clustering. Some material adapted from slides by Andrew Moore, CMU. Visit http://www.autonlab.org/tutorials/ for Andrew’s repository of Data Mining tutorials. Unsupervised Learning. Supervised learning used labeled data pairs (x, y) to learn a function f : X→Y.

488 views • 9 slides

Unsupervised Learning

Unsupervised Learning. Neural Networks 11. Unsupervised learning: simple competitive learning Biological background: Neurons are wired topographically, nearby neurons connect to nearby neurons. In visual cortex, neurons are organized in functional columns.

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Unsupervised learning (II)

Unsupervised learning (II). Topological mapping Kohonen networks (self-organizing maps) Principal components analysis (PCA) Learning algorithms for PCA: Oja’s algorithm Sanger’s algorithm. Topological mapping.

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Unsupervised Learning

Unsupervised Learning. Supervised learning vs. unsupervised learning. Adapted from Andrew Moore, http://www.autonlab.org/tutorials/gmm . Adapted from Andrew Moore, http://www.autonlab.org/tutorials/gmm . Adapted from Andrew Moore, http://www.autonlab.org/tutorials/gmm .

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Unsupervised Learning

Unsupervised Learning. Clustering K-Means. Recall: Key Components of Intelligent Agents. Representation Language: Graph, Bayes Nets, Linear functions Inference Mechanism: A*, variable elimination, Gibbs sampling

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Machine learning: Unsupervised learning

Machine learning: Unsupervised learning. David Kauchak cs311 Spring 2013 adapted from: http://www.stanford.edu/class/cs276/handouts /lecture17-clustering.ppt. Administrative. Assignment 5 out soon. Machine learning code. Weka Java based Tons of approaches

1.01k views • 68 slides

Unsupervised learning

Unsupervised learning. David Kauchak CS 451 – Fall 2013. Administrative. Final project Schedule fo r the rest of the semester. Unsupervised learning. Unsupervised learning: given data, i.e. examples, but no labels. K -means. Start with some initial cluster centers Iterate:

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Unsupervised Learning

Unsupervised Learning. Gaussian Mixture Models Expectation-Maximization (EM). Gaussian Mixture Models. Like K-Means, GMM clusters have centers. In addition, they have probability distributions that indicate the probability that a point belongs to the cluster.

531 views • 32 slides

Unsupervised Learning

Unsupervised Learning. G.Anuradha. Contents. Introduction Competitive Learning networks Kohenen self-organizing networks Learning vector quantization Hebbian learning. Introduction. When no external teacher or critic’s information is available, only input vectors are used for learning

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Unsupervised learning Networks

ELE571 Digital Neural Networks. Unsupervised learning Networks. Associative Memory Networks. Associative Memory Networks. Associative Memory Networks recalls the original undistorted pattern from a distorted or partially-missing pattern. feedforward type (one-shot-recovery)

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Unsupervised Learning

Unsupervised Learning. Find patterns in the data. Group the data into clusters. Many clustering algorithms. K means clustering EM clustering Graph-Theoretic Clustering Clustering by Graph Cuts etc. Clustering by K-means Algorithm.

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Unsupervised Learning

Unsupervised Learning. So far, we have only looked at supervised learning, in which an external teacher improves network performance by comparing desired and actual outputs and modifying the synaptic weights accordingly.

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Unsupervised learning

Unsupervised learning. David Kauchak CS 451 – Fall 2013. Administrative. Final project No office hours today. Supervised learning. label. label 1. model/ predictor. label 3. label 4. label 5. Supervised learning: given labeled examples. Unsupervised learning.

1.14k views • 63 slides

Unsupervised Learning

Unsupervised Learning. A little knowledge…. Agminatics Aciniformics Q-analysis Botryology Systematics Taximetrics Clumping Morphometrics. Nosography Nosology Numerical taxonomy Typology Clustering. A few “synonyms”…. Exploratory Data Analysis.

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Unsupervised Learning Networks

Unsupervised Learning Networks. 主講人 : 虞台文. Content. Introduction Important Unsupervised Learning NNs Hamming Networks Kohonen’s Self-Organizing Feature Maps Grossberg’s ART Networks Counterpropagation Networks Adaptive BAN Neocognitron Conclusion. Unsupervised Learning Networks.

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Unsupervised Learning

Unsupervised Learning. Clustering Unsupervised classification, that is, without the class attribute Want to discover the classes Association Rule Discovery Discover correlation. The Clustering Process. Pattern representation Definition of pattern proximity measure Clustering

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Unsupervised learning

Unsupervised learning. Unsupervised learning is the process of finding structure, patterns or correlation in the given data. we distinguish: Unsupervised Hebbian learning Principal component analysis Unsupervised competitive learning Clustering Data compression.

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Unsupervised Learning

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Unsupervised Learning

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Unsupervised Learning

Unsupervised Learning. Motivation: Given a set of training examples with no teacher or critic, why do we learn? Feature extraction Data compression Signal detection and recovery Self organization Clustering Information can be found about data from inputs. Principal Component Analysis.

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