Assignments
Homework 0: Getting Started with Python [due date: February 4th @5pm]
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 (<20 lines) yourself.
The Colab Notebook for HW0 is available here. Please see Canvas for submission instructions.
Homework 1: Unsupervised Learning [due date (Part 1): February 18th @ 5pm]
Parts 1 and 2 of this assignment is now available on Canvas.
Homework 1 covers methods for Unsupervised Learning. Part 1 will cover methods for cluster analysis, while Part 2 will focus on linear dimensionality reduction methods.
The Colab Notebook for HW1 Part1 is available here. Please see Canvas for submission instructions. [due Feb 18th @ 5pm ET]
The Colab Notebook for HW1 Part 2 is available here. Please see Canvas for submission instructions. [due Feb 23th @ 5pm ET]
Homework 2: Predictive Modeling, Part 1 [due date: March 4th @ 5pm ET]
Homework 2 covers baseline methods for Supervised Learning. Activity 1 will cover methods for linear regression, ridge regression and LASSO, while Part 2 will focus on K-NN for classification.
The Colab Notebook for HW2 is available here. Please see Canvas for submission instructions. [due March 4th @ 5pm ET]
Homework 3: Predictive Modeling, Part 2 [due date: March 25th @ 5pm ET]
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 will ask students to train a model to predict land cover types and submit their predictions to a private Kaggle Competition.
The Colab Notebook for HW3 is available here.
Kaggle competition closes March 26th at 5pm ET. This includes a 24hr grace period -- no late submissions can be accepted after this deadline.