| Students understand and are able to apply fundamental concepts of machine learning, with a focus on model selection, statistical learning, and probabilistic modeling. They can analyze different learning paradigms, optimize model performance, and evaluate generalization error to develop robust machine learning models.
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- Understanding Model Selection Techniques (k5)
Students are able to analyze and apply different model selection strategies, considering factors such as training loss, hyperparameter optimization, and prior knowledge integration.
- Distinguishing Between Supervised and Unsupervised Learning Models (k4)
Students can differentiate between supervised and unsupervised learning models, understanding their theoretical foundations and appropriate use cases.
- Evaluating and Optimizing Generalization Error (k5)
Students are able to estimate and minimize generalization error using techniques like cross-validation, empirical risk minimization, and Bayesian model selection.
- Applying Probability Theory to Model Selection (k5)
Students can utilize probability theory, including Bayesian frameworks, Gaussian distributions, and uncertainty quantification, to improve model reliability.
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Students know of fundamental machine learning concepts, including model selection, training loss functions, generalization error, and probabilistic approaches. They understand how to apply statistical and probabilistic reasoning to develop, evaluate, and optimize machine learning models in a theoretical framework.
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