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Coursera UW Machine Learning Clustering & Retrieval

Course can be found in Coursera

Notebook for quick search can be found in my blog SSQ

Videos in Bilibili(to which I post it)

  • Week 1 Intro

  • Week 2 Nearest Neighbor Search: Retrieving Documents

    • Implement nearest neighbor search for retrieval tasks
    • Contrast document representations (e.g., raw word counts, tf-idf,…)
      • Emphasize important words using tf-idf
    • Contrast methods for measuring similarity between two documents
      • Euclidean vs. weighted Euclidean
      • Cosine similarity vs. similarity via unnormalized inner product
    • Describe complexity of brute force search
    • Implement KD-trees for nearest neighbor search
    • Implement LSH for approximate nearest neighbor search
    • Compare pros and cons of KD-trees and LSH, and decide which is more appropriate for given dataset
    • Choosing features and metrics for nearest neighbor search
    • Implementing Locality Sensitive Hashing from scratch

  • Week 3 Clustering with k-means

    • Describe potential applications of clustering
    • Describe the input (unlabeled observations) and output (labels) of a clustering algorithm
    • Determine whether a task is supervised or unsupervised
    • Cluster documents using k-means
    • Interpret k-means as a coordinate descent algorithm
    • Define data parallel problems
    • Explain Map and Reduce steps of MapReduce framework
    • Use existing MapReduce implementations to parallelize kmeans, understanding what’s being done under the hood
    • Clustering text data with k-means

  • Week 4 Mixture Models: Model-Based Clustering

    • Interpret a probabilistic model-based approach to clustering using mixture models
    • Describe model parameters
    • Motivate the utility of soft assignments and describe what they represent
    • Discuss issues related to how the number of parameters grow with the number of dimensions
      • Interpret diagonal covariance versions of mixtures of Gaussians
    • Compare and contrast mixtures of Gaussians and k-means
    • Implement an EM algorithm for inferring soft assignments and cluster parameters
      • Determine an initialization strategy
      • Implement a variant that helps avoid overfitting issues
    • Implementing EM for Gaussian mixtures
    • Clustering text data with Gaussian mixtures

  • Week 5 Latent Dirichlet Allocation: Mixed Membership Modeling

    • Compare and contrast clustering and mixed membership models
    • Describe a document clustering model for the bagof-words doc representation
    • Interpret the components of the LDA mixed membership model
    • Analyze a learned LDA model
      • Topics in the corpus
      • Topics per document
    • Describe Gibbs sampling steps at a high level
    • Utilize Gibbs sampling output to form predictions or estimate model parameters
    • Implement collapsed Gibbs sampling for LDA
    • Modeling text topics with Latent Dirichlet Allocation

  • Week 6 Hierarchical Clustering & Closing Remarks

    • Bonus content: Hierarchical clustering
      • Divisive clustering
      • Agglomerative clustering
        • The dendrogram for agglomerative clustering
        • Agglomerative clustering details
    • Hidden Markov models (HMMs): Another notion of “clustering”
    • Modeling text data with a hierarchy of clusters