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Course Outline

  1. Distributed Systems in Big Data
    1. Data Mining Methods (Single-Machine Training + Distributed Prediction: Traditional Machine Learning Algorithms + MapReduce-based Distributed Prediction)
    2. Apache Spark MLlib
  2. Recommendation and Precision Advertising:
    1. Aspects of Natural Language
    2. Text Clustering, Text Classification (Labeling), and Synonym Detection
    3. User Profile Reconstruction and Tagging Systems
    4. Strategies for Recommendation Algorithms
    5. Inter-class Lift, Intra-class Lift, and Achieving Precision
    6. Building a Closed Loop for Recommendation Algorithms
  3. Logistic Regression and Ranking SVM
  4. Feature Recognition: (Automatic Feature Extraction via Deep Learning and Image Processing)
  5. Natural Language
    1. Chinese Word Segmentation
    2. Topic Modeling (Text Clustering)
    3. Text Classification
    4. Keyword Extraction
    5. Semantic Analysis: Semantic Parser, from Word2Vec to Word Vectors
    6. RNN: Long Short-Term Memory (LSTM) Architecture
 21 Hours

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