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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 and Precise Ad Targeting:
    1. Partial aspects of Natural Language
    2. Text clustering, text classification (tagging), synonyms
    3. User profile restoration and tag system construction
    4. Strategies for recommendation algorithms
    5. Lift between classes, lift within classes, and how to achieve precision
    6. How to build a closed-loop for recommendation algorithms
  3. Logistic Regression, RankingSVM
  4. Feature Recognition: (Automatic feature recognition through 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 parsing, from Word2Vec to word vectors
    6. RNN Long Short-Term Memory (LSTM) Architecture

Requirements

There are no specific requirements to participate in this course.

 21 Hours

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