University of Washington - Machine Learning: Regression

INSTRUCTORS

Instructors: Carlos Guestrin and Emily Fox

In this course, you will explore regularized linear regression models for the task of prediction and feature selection. You will be able to handle very large sets of features and select between models of various complexity. You will also analyze the impact of aspects of your data – such as outliers – on your selected models and predictions. To fit these models, you will implement optimization algorithms that scale to large datasets.

Learning Outcomes:

Describe the input and output of a regression model.

Compare and contrast bias and variance when modeling data.

Estimate model parameters using optimization algorithms.

Tune parameters with cross validation.

Analyze the performance of the model.

Describe the notion of sparsity and how LASSO leads to sparse solutions.

Deploy methods to select between models.

Exploit the model to form predictions.

Build a regression model to predict prices using a housing dataset.

Implement these techniques in Python.