ML: Clustering & Retrieval
Using k-nearest neighbors, k-means, and probabilistic modeling techniques, participants will use Python to develop clustering and document retrieval systems.
Description for ML: Clustering & Retrieval
Features of the Course:
Methods of Document Retrieval: Develop a document retrieval system that employs k-nearest neighbors and employs methods to reduce computational burden, such as KD-trees and locality-sensitive hashing.
Approaches to Clustering: Utilize k-means to cluster documents by topic, and employ MapReduce to parallelize the process. Additionally, investigate probabilistic clustering with mixture models.
Proficient Probabilistic Modeling: Fit Gaussian mixture models through expectation maximization (EM) with Gibbs sampling, and perform mixed membership modeling using latent Dirichlet allocation (LDA).
Optimization and Implementation: Compare the initialization methods for non-convex optimization objectives and implement these clustering and modeling techniques in Python.
Level: Intermediate
Certification Degree: Yes
Languages the Course is Available: 22
Offered by: On Coursera provided by University of Washington
Duration: 3 weeks at 5 hours a week
Schedule: Flexible
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