What will you in the Machine Learning: Clustering & Retrieval Course
Implement document retrieval systems using k-nearest neighbors (k-NN).
Identify and apply various similarity metrics for text data.
Optimize k-NN search using KD-trees and locality-sensitive hashing (LSH).
Cluster documents by topic using k-means and parallelize it with MapReduce.
Explore probabilistic clustering with mixture models and expectation maximization (EM).
Perform mixed membership modeling using latent Dirichlet allocation (LDA).
Understand and implement Gibbs sampling for inference in topic models.
Compare supervised and unsupervised learning tasks in the context of information retrieval.
Program Overview
Module 1: Introduction to Clustering and Retrieval
Overview of clustering and retrieval tasks in machine learning.
Introduction to the course structure and prerequisites.
Module 2: Nearest Neighbor Search
Implementing k-NN for document retrieval.
Optimizing search with KD-trees and LSH.
Module 3: Clustering
Applying k-means clustering to group similar documents.
Parallelizing k-means using MapReduce for scalability.
Module 4: Mixture Models and EM
Understanding probabilistic clustering approaches.
Fitting mixture of Gaussian models using EM algorithm.
Module 5: Topic Modeling with LDA
Performing mixed membership modeling using LDA.
Implementing Gibbs sampling for inference in topic models.
Module 6: Case Study and Applications
Applying learned techniques to real-world document retrieval scenarios.
Comparing and contrasting supervised and unsupervised learning tasks.
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Job Outlook
Data Scientists: Enhance skills in clustering and retrieval techniques for large datasets.
Machine Learning Engineers: Implement efficient search and recommendation systems.
NLP Specialists: Apply topic modeling and similarity measures in text analysis.
Information Retrieval Engineers: Design and optimize document retrieval systems.
AI Researchers: Explore advanced clustering algorithms and probabilistic models.
Specification: Machine Learning: Clustering & Retrieval
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