IE 583 Knowledge Discovery and Data Mining

Schedule (Back)

  1. Introduction to Knowledge Discovery
  2. Supervised Learning
    3. Naive Bayes Classifier, Decision Trees, Instance-Based Learning, Meta-Learners, Minimum Description Length (MDL) Principle, Selecting Models. Introduction to Weka.
  3. Data Engineering
    4. Data Selection & Cleaning, Exploratory Data Mining, Attribute Selection.
  4. Unsupervised Learning
    5. Clustering, Association Rule Discovery
  5. Text Mining

Complex Data Types, Text Mining, Web Mining

  1. Optimization Methods in Learning

    2. Introduction to Optimization, Heuristic Random Search, Support Vector Machines, Optimizing Learning Parameters

  2. CRISP Data Mining Process