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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. Recommendation and Precision Ad Placement:
    1. Partial Aspects of Natural Language
    2. Text Clustering, Text Classification (Labels), Synonyms
    3. User Profile Restoration, Label Systems
    4. Recommendation Algorithm Strategies
    5. Lift Between Categories, Lift Within Categories, How to Achieve Precision
    6. How to Build a Closed-Loop for Recommendation Algorithms
  3. Logistic Regression, RankingSVM,
  4. Feature Recognition: (Automatic Feature Recognition with 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 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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