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.