Introduces nonlinear ML algorithms, covering nearest neighbor, k-NN, polynomial curve fitting, model complexity, overfitting, and regularization.
Explores probabilistic linear regression, covering joint and conditional probability, ridge regression, and overfitting mitigation.
Covers the basics of linear regression, binary and multi-class classification, and evaluation metrics.
Covers linear regression, regularization, inverse problems, X-ray tomography, image reconstruction, data inference, and detector intensity.
Introduces Natural Language Processing, covering text preprocessing, sentiment analysis, and topic analysis, with a focus on building a climate change risk index.
