Machine Learning: Clustering & Retrieval

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6 序列
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Week 1 - Welcome
Clustering and retrieval are some of the most high-impact machine learning tools out there. Retrieval is used in almost every applications and device we interact with, like in providing a set of products related to one a shopper is currently considering, or a...
Week 2 - Nearest Neighbor Search
We start the course by considering a retrieval task of fetching a document similar to one someone is currently reading. We cast this problem as one of nearest neighbor search, which is a concept we have seen in the Foundations and Regression courses. However...
Week 3 - Clustering with k-means
In clustering, our goal is to group the datapoints in our dataset into disjoint sets. Motivated by our document analysis case study, you will use clustering to discover thematic groups of articles by "topic". These topics are not provided in this unsupervise...
Week 4 - Mixture Models
In k-means, observations are each hard-assigned to a single cluster, and these assignments are based just on the cluster centers, rather than also incorporating shape information. In our second module on clustering, you will perform probabilistic model-based ...
Week 5 - Mixed Membership Modeling via Latent Dirichlet Allocation
The clustering model inherently assumes that data divide into disjoint sets, e.g., documents by topic. But, often our data objects are better described via memberships in a collection of sets, e.g., multiple topics. In our fourth module, you will explore lat...
Week 6 - Hierarchical Clustering & Closing Remarks
In the conclusion of the course, we will recap what we have covered. This represents both techniques specific to clustering and retrieval, as well as foundational machine learning concepts that are more broadly useful.
We provide a quick tour into an altern...