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|CS 50025 - Foundations Of Decision Making|
Learning Outcomes: 1. Support and justify claims with evidence from data and discuss how human biases can impact interpretations. 2. Recognize the basic approach to formulating statistical hypothesis tests and apply parametric hypothesis tests for both discrete and continuous data. 3. Interpret p-values and apply them effectively to support claims about data. 4. Formulate and test conjectures with controlled experiments, including A/B testing. 5. Recognize the types of errors encountered when conducting multiple hypothesis tests. 6. Use computational approaches based on randomization for hypothesis testing and uncertainty quantification. 7. Discuss ethical issues such as privacy and fairness that can be associated with automating decisions based on machine learning methods. 8. Apply the basic components of classification methods using Python libraries. 9. Evaluate the performance of learned models with learning curves and k-fold cross validation and assess significance with hypothesis tests.
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