This repo contains the slides and worksheets for Boston University's CS 506 course and aims to:

1. Centralize all the content for the course
2. Make the content more widely accessible
3. Allow students to get ahead or catch up

0. Course Overview
1. Git / GitHub
2. Clean Code (Engineering Best Practices)
3. Introduction to Data Science
4. Distance & Similarity
5. Clustering (Kmeans)
6. Clustering (Kmeans++ & Hierarchical Clustering)
7. Clustering (DBScan)
8. Clustering (Gaussian Mixture Model)
9. Clustering Aggregation
10. Singular Value Decomposition
11. Latent Semantic Analysis
12. Intro to Classification & K Nearest Neighbors
13. Decision Trees
14. Naive Bayes & Model Evaluation & Ensemble Methods
15. Support Vector Machines (Linear)
16. Support Vector Machines (Non-Linear)
17. Recommender Systems
18. Linear Regression
19. Logistic Regression
20. Gradient Descent
21. Linear Model Evaluation (Hypothesis Testing)
22. Linear Model Evaluation (Confidence Intervals & Checking Assumptions)
23. Neural Networks
24. How to Tune Neural Networks
25. Types of Neural Networks
26. Generative Adversarial Networks

This repo is updated every semester.