Course Outline

  1. Data preprocessing

    1. Data Cleaning
    2. Data integration and transformation
    3. Data reduction
    4. Discretization and concept hierarchy generation
  2. Statistical inference

    1. Probability distributions, Random variables, Central limit theorem
    2. Sampling
    3. Confidence intervals
    4. Statistical Inference
    5. Hypothesis testing
  3. Multivariate linear regression

    1. Specification
    2. Subset selection
    3. Estimation
    4. Validation
    5. Prediction
  4. Classification methods

    1. Logistic regression
    2. Linear discriminant analysis
    3. K-nearest neighbours
    4. Naive Bayes
    5. Comparison of Classification methods
  5. Neural Networks

    1. Fitting neural networks
    2. Training neural networks issues
  6. Decision trees

    1. Regression trees
    2. Classification trees
    3. Trees Versus Linear Models
  7. Bagging, Random Forests, Boosting

    1. Bagging
    2. Random Forests
    3. Boosting
  8. Support Vector Machines and Flexible disct

    1. Maximal Margin classifier
    2. Support vector classifiers
    3. Support vector machines
    4. 2 and more classes SVM’s
    5. Relationship to logistic regression
  9. Principal Components Analysis

  10. Clustering

    1. K-means clustering
    2. K-medoids clustering
    3. Hierarchical clustering
    4. Density based clustering
  11. Model Assesment and Selection

    1. Bias, Variance and Model complexity
    2. In-sample prediction error
    3. The Bayesian approach
    4. Cross-validation
    5. Bootstrap methods
 28 Hours

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