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Course Outline

  1. Distributed AI under Big Data
    1. Data Mining Methods (Training single model + distributed prediction: Traditional machine learning algorithms + MapReduce distributed prediction,)
    2. Apache Spark MLlib
  2. Recommendation and precise ad targeting:
    1. Natural Language Processing
    2. Text clustering, text classification (tagging), synonyms
    3. User profile restoration, tag system
    4. Recommendation algorithm strategies
    5. Lift between categories, lift within categories, how to be precise
    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 Processing
    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 (TSTM) Architecture

Requirements

There are no specific requirements to participate in this course.

 21 Hours

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