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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 precise advertising:
    1. Partial natural language processing
    2. Text clustering, text classification (labeling), synonyms
    3. User profile reconstruction, tagging systems
    4. Strategies for recommendation algorithms
    5. Lift between classes, lift within classes, how to achieve precision
    6. How to build a closed loop for recommendation algorithms
  3. Logistic Regression, RankingSVM,
  4. Feature identification: (automatic 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 parser, word2vec to word vectors
    6. RNN Long short-term memory (LSTM) Architecture

Requirements

There are no specific prerequisites for participating in this course.

 21 Hours

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