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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 Delivery:
    1. Natural Language Processing
    2. Text Clustering, Text Classification (Labels), Synonyms
    3. User Profile Reconstruction, Labeling Systems
    4. Strategies for Recommendation Algorithms
    5. Lift between classes, Lift within classes, Precision
    6. Building the Closed Loop for Recommendation Algorithms
  3. Logical Regression, RankingSVM
  4. Feature Recognition: (Automatic Feature Recognition with 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 (LSTM) Architecture

Requirements

There are no specific requirements to participate in this course.

 21 Hours

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