EE-311: Fundamentals of machine learning

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Lectures in this course (48)

Maximum Likelihood Estimation: Density Estimation and Bernoulli Model

Explores maximum likelihood estimation for density and Bernoulli model, including test reliability and disease screening.

Bayes Estimator: Definition and Application

Introduces the Bayes estimator, explaining its definition, application in quadratic cost scenarios, and importance in probabilistic reasoning.

Maximum Likelihood Decision

Explains decision by maximum likelihood and the use of logarithms.

Legal Implications of Data Processing and AI

Explores legal obligations and ethical considerations in data processing and AI, covering consumer credit, data protection, and automated decision-making.

Model Selection and Evaluation: Bias-Variance Dilemma, Ridge Estimation

Explores over-learning, generalization, and under-learning in machine learning models.

Model Selection: Evaluation and Generalization

Explores model selection, evaluation, and generalization in machine learning, emphasizing unbiased performance estimation and the risks of over-learning.

Performance Criteria: Confusion Matrix, Recall, Precision, Accuracy

Explores performance criteria in supervised learning, emphasizing precision, recall, and specificity in model evaluation.

Model Selection: ROC Curves, Regression Evaluation, Conclusion

Explores thresholding, ROC curves, regression evaluation, naive methods, and model selection in machine learning.

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