Machine Learning:clustering & Retrieval Learning Clustering and Information retrieval (framework)

Case Studies:finding Similar Documents
Learning Outcomes:by The end of this course, you'll be a able to: (Learn through this chapter, you'll Master)
-create a document retrieval system using K-nearest neighbors. Constructing text retrieval systems with K neighbors
-identify various similarity metrics for text data. Text Similarity matrix
-reduce computations in K-nearest neighbor search by using kd-trees. Reducing the computational complexity of K-nearest neighbor search using KD tree
-produce approximate nearest neighbors using locality sensitive hashing. Based on locally sensitive Hashishen into nearest neighbor
-compare and contrast supervised and unsupervised learning tasks. Supervisory and unsupervised learning tasks
-cluster documents by topic using K-means. Document topic Clustering based on K-mean
-describe How to parallelize K-means using MapReduce. parallelization of K-mean using MapReduce
-examine probabilistic clustering approaches using mixtures models. Mixed Model Clustering
-fit a mixture of Gaussian model using expectation maximization (EM). Use em to fit Gaussian mixture model
-perform mixed membership modeling using latent Dirichlet allocation (LDA). LDA-based
-describe the steps of a Gibbs sampler and how to use it output to draw inferences. Gibbs sampling
-compare and contrast initialization techniques for Non-convex optimization objectives. Comparison of non-convex optimization techniques
-implement These techniques in Python implements the above content in Python

========================================================================================================
                        ########### #chapter2: Nearest Neighbor search#############
=========================== =============================================================================
Introduction to nearest neighbor Search and algorithms neighbor searches and algorithms describes
the importance of data representations and distance metrics data representation and the importance of distance measurement
Programming Assignment 1 Programming task 1
Scaling up K-nn search using kd-trees based on KD tree for K-nearest neighbor Search
Locality sensitive hashing for APPR Oximate NN search based on a local sensitive hash to implement a near lookup
Programming Assignment 2 Programming task 2
summarizing nearest neighbor Search summary

========================================================================================================
                        ########### #chapter3: Clustering with k-means#############
=========================== =============================================================================
Introduction to Clustering cluster introduction
Clustering via K-meansk mean clustering
Programming assignment programming task
MapReduce for scaling K-means
Summarizing Clustering with K-means summary

========================================================================================================
########### #chapter4: Mixture models#############
========================================================================================================
Motivating and setting the foundation for mixture models hybrid model
Mixtures of Gaussians for clustering Gaussian mixture model
Expectation maximization (EM) building blocks desired maximum
The EM algorithm EM algorithm
Summarizing mixture Models Summary
Programming Assignment 1
Programming Assignment 2

========================================================================================================
########### #chapter5: Mixed membership Modeling via latent Dirichlet allocation#############
========================================================================================================
Introduction to latent Dirichlet allocation LDA introduction
Bayesian inference via Gibbs sampling Bayesian inference based on Gibbs sampling
Gibbs sampling of collapsed Gibbs sampling for LDA LDA
Summarizing latent Dirichlet allocation summary
Programming assignment
========================================================================================================
########### #chapter6: Hierarchical Clustering & Closing remarks#############
========================================================================================================
What we ' ve learned
Hierarchical clustering and clustering for time series segmentation hierarchical clustering and clustering based on temporal sequence segmentation
Programming assignment
Summary and what's ahead in the specialization summary

Machine Learning:clustering & Retrieval Learning Clustering and Information retrieval (framework)