Nonlinear ML AlgorithmsIntroduces nonlinear ML algorithms, covering nearest neighbor, k-NN, polynomial curve fitting, model complexity, overfitting, and regularization.
Regression: Linear ModelsIntroduces linear regression, generalized linear models, and mixed-effect models for regression analysis.
Probabilistic Linear RegressionExplores probabilistic linear regression, covering joint and conditional probability, ridge regression, and overfitting mitigation.
Bias-Variance Trade-OffExplores underfitting, overfitting, and the bias-variance trade-off in machine learning models.
Machine Learning BasicsIntroduces the basics of machine learning, covering supervised and unsupervised learning, linear regression, and data understanding.
