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Course Outline

  1. Distributed Computing in the Big Data Era
    1. Data mining methods (training single models + distributed prediction: traditional machine learning algorithms + MapReduce distributed prediction)
    2. Apache Spark MLlib
  2. Recommendation and Precision Advertising:
    1. Components of Natural Language
    2. Text clustering, text classification (labeling), synonyms
    3. User profile restoration, tagging systems
    4. Strategies for recommendation algorithms
    5. Lift between classes, lift within classes, and how to achieve precision
    6. How to construct the closed loop for recommendation algorithms
  3. Logistic Regression, RankingSVM,
  4. Feature Recognition: (automatic feature recognition with deep learning and graphs)
  5. Natural Language Processing
    1. Chinese word segmentation
    2. Topic models (text clustering)
    3. Text classification
    4. Keyword extraction
    5. Semantic analysis: semantic parser, Word2Vec to word vectors
    6. RNN Long Short-Term Memory (LSTM) Architecture

Requirements

There are no specific requirements to participate in this course.

 21 Hours

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