Description

In this course, you will learn

• Introduction to machine learning..
• Linear prediction.
• Maximum likelihood and linear prediction.
• Ridge, nonlinear regression with basis functions and Cross-validation.
• Bayesian learning.
• Gaussian processes for nonlinear regression.
• Bayesian optimization, Thompson sampling and bandits.
• Decision trees.
• Random forests.
• Spring break.
• Random forests applications.
• Unconstrained optimization.
• Gradient descent and Newton's method.
• Logistic regression, IRLS and importance sampling.
• Neural networks.
• Deep learning.
• Importance sampling and MCMC.
• Constrained optimization, Lagrangians and duality.
• Application to penalized maximum likelihood and Lasso.