Skip to main content
Back to top
Ctrl
+
K
Welcome to DS325 Applied Data Science
Setting Up and Resources
1. Setting Up
2. Our course GenAI Policy
3. Linear Regression Review Topics
4. Review Materials
Regression
1. What is a model?
2. Linear Regression (Part 1)
3. Linear Regression (Part 2)
4. Bias, Variance, and Regularization
5. Regularization Example: King County housing
6. Polynomial Regression
Classification
1. Classification
2. Logistic Regression
3. Trees and Ensemble methods
4. Tree Methods (continued)
6. Forests and Trees
7. Principle Component Analysis (PCA)
8. PCA: Examples and Observations
9. K-Nearest Neighbors (KNN) (a lazy learner)
10. K-Means Clustering
11. Data Storytelling
Neural Networks
1. Neural Networks, Intro and Intuition
2. Neural Networks with Keras
Assignments
Github Submission Guide
PS00
PS00 (Solutions by Prof Roth)
PS01 - (Not so) Simple Linear Regression
PS02 - Regularization
Mini-Project 1 (and examples!)
PS03 - Classification
Crack the Code!
ps04 - Group data storytelling
Extra Credit
Final Project
Repository
Open issue
Index
