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Course Outline

  1. Distributed Computing Under Big Data
    1. Data mining methods (training single models + distributed prediction: traditional machine learning algorithms + MapReduce distributed prediction)
    2. Apache Spark MLlib
  2. Recommendation and Precision Ad Targeting:
    1. Subdomains of natural language
    2. Text clustering, text classification (tagging), synonyms
    3. User profile reconstruction, tag systems
    4. Strategies for recommendation algorithms
    5. Lift between classes, intra-class lift, how to achieve precision
    6. How to construct the closed loop of recommendation algorithms
  3. Logistic Regression, RankingSVM,
  4. Feature Recognition: (automated feature recognition with deep learning and graphs)
  5. Natural Language
    1. Chinese word segmentation
    2. Topic models (text clustering)
    3. Text classification
    4. Keyword extraction
    5. Semantic analysis: semantic parsers, 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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