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Course Outline

  1. Distributed in the Big Data Era
    1. Data Mining Methods (training single models + distributed prediction: traditional machine learning algorithms + MapReduce distributed prediction)
    2. Apache Spark MLlib
  2. Recommendation and Precision Advertising:
    1. Natural Language Processing
    2. Text Clustering, Text Classification (labeling), Synonyms
    3. User Profile Restoration, Tag Systems
    4. Strategies of Recommendation Algorithms
    5. Inter-category lift, intra-category lift, and how to achieve precision
    6. How to construct the closed-loop of recommendation algorithms
  3. Logistic Regression, RankingSVM
  4. Feature Recognition (Deep Learning and Automatic Feature Recognition of 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 prerequisites for participating in this course.

 21 Hours

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