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Course Outline

  1. Distributed systems under Big Data
    1. Data mining methods (training single models + distributed prediction: traditional machine learning algorithms + Mapreduce distributed prediction,)
    2. Apache Spark MLlib
  2. Recommendations and precise ad targeting:
    1. Partial aspects of natural language
    2. Text clustering, text classification (labeling), synonyms
    3. User profile reconstruction, tag systems
    4. Strategies for recommendation algorithms
    5. Lift between categories, within-category lift, how to achieve precision
    6. How to build a closed loop for recommendation algorithms
  3. Logistic regression, RankingSVM,
  4. Feature recognition: (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
 21 Hours

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