Interpretable machine learning applications: Part 5

Instructor: Epaminondas Kapetanios

What you'll learn

  •    Be acquainted with the basics of the Aequitas Tool as a tool to measure and detect bias in the outcome of a machine learning prediction model.
  •   Learn more about a real world case study, i.e., predictions of recidivism (COMPAS dataset), and how the prediction model may have been biased.
  •   Learn a technique, which is largely based on statistical descriptors, for measuring bias and fairness for Machine Learning (ML) prediction models.

    • Skills you'll practice

  •   Predictive Modeling
  •   Data Visualization
  •   Statistical Methods
  •   Applied Machine Learning
  •   Public Policies
  •   Policy Analysis
  •   Probability & Statistics
  •   Descriptive Statistics
  •   Data Ethics
  •   Machine Learning
  •   Exploratory Data Analysis
  •   Anomaly Detection

    • Interpretable Machine Learning Applications: Part 2

    Interpretable Machine Learning Applications: Part 1

    Interpretable machine learning applications: Part 3

    Interpretable Machine Learning Applications: Part 4

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