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Course Outline

  1. Distributed Processing 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 and Precise Ad Targeting:
    1. Partial processing of natural language
    2. Text clustering, text classification (labelling), and synonym identification
    3. User profile reconstruction and label system construction
    4. Strategies for recommendation algorithms
    5. Lift within classes, lift between classes, and achieving precision
    6. Constructing the feedback loop for recommendation algorithms
  3. Logistic Regression, RankingSVM
  4. Feature Recognition: (Automatic feature recognition using Deep Learning and Graph techniques)
  5. Natural Language Processing
    1. Chinese word segmentation
    2. Topic models (text clustering)
    3. Text classification
    4. Keyword extraction
    5. Semantic analysis: semantic parsers and Word2Vec 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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