Major ML Techniques:

  • Regression: predicting continuous values
  • Classification: predicting the item class, category of a case
  • Clustering: finding the structure of data, summarization, anomaly detection
  • Associations: associating frequent co-occurring items, events
  • Anomaly Detection: discovering abnormal and unusual cases
  • Sequence Mining: predicting next events, click-stream (Markov Model)
  • Dimension Reduction: reducing the size of data (PCA)
  • Recommendation Systems: recommending items

Recommender Systems:

Three types of recommender systems:

  1. Content-base
  2. Collaborative Filtering
  3. Hybrid Recommender Systems

Implementing Recommender Systems:

  1. Memory-based: uses the entire user-item dataset to generate a recommendation, uses statistical techniques to approximate users or items (e.g. Pearson correlation, cosine similarity, Euclidean distance, etc).

Eva W.

UC San Diego, Computational Science (AI & HPC)

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