gizemgokce/coursera-uw-machine-learning-clustering-retrieval

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