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Course Outline

  1. Distributed 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 targeted advertising:
    1. Part of natural language
    2. Text clustering, text classification (labels), synonyms
    3. User profile restoration, tag system
    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 recognition: (automatic feature recognition of deep learning and graphs)
  5. Natural language
    1. Chinese word segmentation
    2. Topic models (text clustering)
    3. Text classification
    4. Extracting keywords
    5. Semantic analysis: semantic parser, word2vec to word vectors
    6. RNN Long short-term memory (TSTM) Architecture
 21 Hours

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