Machine Learning: Clustering & Retrieval
Master clustering algorithms and retrieval techniques for machine learning applications, from k-means to latent Dirichlet allocation.
Master clustering algorithms and retrieval techniques for machine learning applications, from k-means to latent Dirichlet allocation.
This course cannot be purchased separately - to access the complete learning experience, graded assignments, and earn certificates, you'll need to enroll in the full Machine Learning Specialization program. You can audit this specific course for free to explore the content, which includes access to course materials and lectures. This allows you to learn at your own pace without any financial commitment.
4.7
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English
پښتو, বাংলা, اردو, 2 more
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What you'll learn
Create document retrieval systems using k-nearest neighbors
Implement clustering algorithms with k-means and EM
Apply locality sensitive hashing for efficient search
Develop probabilistic clustering models
Build mixed membership models using LDA
Scale clustering solutions using MapReduce
Skills you'll gain
This course includes:
6.5 Hours PreRecorded video
15 assignments
Access on Mobile, Tablet, Desktop
FullTime access
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There are 6 modules in this course
This comprehensive course explores advanced machine learning techniques for clustering and retrieval tasks. Students learn to implement various algorithms including k-nearest neighbors, k-means clustering, expectation maximization, and latent Dirichlet allocation. The course covers practical applications in document analysis, image clustering, and text mining. Through hands-on programming assignments, learners develop skills in building scalable solutions using MapReduce and other optimization techniques.
Welcome
Module 1 · 1 Hours to complete
Nearest Neighbor Search
Module 2 · 5 Hours to complete
Clustering with k-means
Module 3 · 2 Hours to complete
Mixture Models
Module 4 · 3 Hours to complete
Mixed Membership Modeling via Latent Dirichlet Allocation
Module 5 · 2 Hours to complete
Hierarchical Clustering & Closing Remarks
Module 6 · 1 Hours to complete
Fee Structure
Instructors
Leader in Machine Learning and Intelligent Applications
Carlos Guestrin is the Amazon Professor of Machine Learning at the University of Washington's Computer Science & Engineering Department. He is also the co-founder and CEO of Dato, Inc., which focuses on facilitating the development of intelligent applications utilizing large-scale machine learning. Prior to his current role, Guestrin served as the Finmeccanica Associate Professor at Carnegie Mellon University and was a senior researcher at Intel Research Lab in Berkeley.
Expert in Machine Learning and Bayesian Modeling
Emily Fox is an assistant professor and the Amazon Professor of Machine Learning in the Statistics Department at the University of Washington. Previously, she was a faculty member in the Wharton Statistics Department at the University of Pennsylvania. Fox has received several prestigious awards, including the Sloan Research Fellowship, a Young Investigator Award from the U.S. Office of Naval Research, and a National Science Foundation CAREER Award.
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4.7 course rating
2,354 ratings
Frequently asked questions
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