This first assignment will help to familiarize you with the Python programming language and Google Colab notebooks. For this assignment you will primarily be running code examples, but you will also write a little bit of Python code yourself.
The Colab Notebook for HW0 is available here. Please see Canvas for submission instructions.
Homework 1 covers methods for Unsupervised Learning. This assignment is split into two Colab Notebooks. Activity 1 will cover methods for cluster analysis, while Activity 2 will focus on linear dimensionality reduction methods.
The Colab Notebook for HW1 Activity 1 (Clustering) is available here.
The Colab Notebook for HW1 Activity 1 (PCA & NMF) is available here.
Please see Canvas for submission instructions.
Homework 2 covers baseline models for Supervised Learning. In the first activity, you will train linear regression, ridge regression and LASSO regression models to predict wind and solar energy production based on weather variables. In the second activity, you will use K-Nearest Neighbors to classify birds based on their bone measurements. In both activities, you will use cross-validation to select model parameters.
The Colab Notebook for HW2 is available here. See Canvas for submission instructions.
Homework 3 covers classification and regression methods including SVMs, neural networks, decisions trees and random forests. Activity 1 will compare multiple classifiers on the same data set. Activity 2 will focus on tree-based methods. Activity 3 (separate notebook) will be an open-ended landcover classification challenge administered via the Kaggle data science competition website.
The Colab Notebook for HW3 (Activities 1 & 2) is available here.
The Colab Notebook for HW3 (Kaggle Competition) is available here.
Note: The Kaggle competition has a hard deadline. You have three full weeks to complete this part of the assignment, so don't wait until the last minute.
See Canvas for submission instructions.
Homework 4 requires students to answer discussion questions on a selected set of readings prior to in-class group discussions the week of May 2nd.
Instructions and discussion questions can be found in the following Google Doc.
Note: Students who do not participate in the group discussions must complete an alternate assignment.
See Google Doc for submission instructions.