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Course Outline

  1. Distributed Systems in Big Data
    1.  Data mining methods (training single models + distributed prediction: traditional machine learning algorithms + MapReduce distributed prediction,)
    2. Apache Spark MLlib
  2. Recommendation Systems and Targeted Advertising:
    1. Parts of Natural Language Processing
    2. Text clustering, text classification (tagging), and synonyms
    3. User profile reconstruction and tag systems
    4. Strategies for recommendation algorithms
    5. Lift between classes, lift within classes, and how to achieve precision
    6. How to build a closed-loop system for recommendation algorithms
  3. Logistic Regression, RankingSVM,
  4. Feature Recognition: (Automatic feature recognition through deep learning and graphs)
  5. Natural Language Processing
    1. Chinese word segmentation
    2. Topic models (text clustering)
    3. Text classification
    4. Keyword extraction
    5. Semantic analysis: semantic parsing, from word2vec to word vectors
    6. RNN Long Short-Term Memory (LSTM) Architecture
 21 Hours

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