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    Predictive Modeling
    Robbie Beane

    Robbie Beane

    Assistant Professor of Data Science
    Program Director for Computer Science
    Maryville University

    • St. Louis, MO
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    Predictive Modeling

    This page contains links to html renders of RStudio notebooks that I developed in Fall 2019 for the following two courses offered at Maryville University:

    • DSCI 412 - Predictive Modeling (Undergraduate)
    • DSCI 512 - Predictive Modeling (Graduate)

    These courses introduce the theory of several machine learning and statistical learning techniques. They also cover the use of R for performing tasks related to the techniques.

    • 1.1 - Introduction to Statistical Learning
    • 2.1 - Introduction to Supervised Learning
    • 2.2 - Training Regression Models
    • 2.3 - Bias-Variance Trade-Off
    • 3.1 -Simple Linear Regression
    • 3.1.a - Derivation of Parameter Estimates
    • 3.1.b - Derivation of R-Squared
    • 3.2 - Assumptions about Error Term
    • 3.3 - Inference in SLR
    • 3.4 - Multiple Regression
    • 3.5 - Categorical Predictors
    • 3.6 - Logarithmic Transformations
    • 4.1 - Logistic Regression
    • 4.2 - Multinomial Logistic Regression
    • 4.3 - KNN Classification
    • 5.1 - Introduction to Cross Validation
    • 5.2 - Introduction to Caret
    • 6.1 - Ridge and LASSO Regression
    • 6.2 - Regularized Logistic Regression
    • 8.1 - Decision Trees
    • 8.2 - Random Forests
    • 10.1 - Principal Component Analysis
    • 10.2 - PCA for Facial Recognition
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