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Course Outline

  1. Distributed
    1. Data mining methods (training single models + distributed prediction: traditional machine learning algorithms + MapReduce distributed prediction)
    2. Apache Spark MLlib
  2. Recommendation and precise ad targeting:
    1. Part of natural language
    2. Text clustering, text classification (tagging), synonyms
    3. User profile reconstruction, tag system
    4. Strategies for recommendation algorithms
    5. Lift between categories, lift within categories, how to achieve precision
    6. How to build a closed loop for recommendation algorithms
  3. Logistic regression, RankingSVM,
  4. Feature recognition: (automatic feature recognition in deep learning and graphics)
  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
 21 Hours

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