This repo contains the slides and worksheets for Boston University's CS 506 course and aims to:
- Centralize all the content for the course
- Make the content more widely accessible
- Allow students to get ahead or catch up
- Course Overview
- Git / GitHub
- Clean Code (Engineering Best Practices)
- Introduction to Data Science
- Distance & Similarity
- Clustering (Kmeans)
- Clustering (Kmeans++ & Hierarchical Clustering)
- Clustering (DBScan)
- Clustering (Gaussian Mixture Model)
- Clustering Aggregation
- Singular Value Decomposition
- Latent Semantic Analysis
- Intro to Classification & K Nearest Neighbors
- Decision Trees
- Naive Bayes & Model Evaluation & Ensemble Methods
- Support Vector Machines (Linear)
- Support Vector Machines (Non-Linear)
- Recommender Systems
- Linear Regression
- Logistic Regression
- Gradient Descent
- Linear Model Evaluation (Hypothesis Testing)
- Linear Model Evaluation (Confidence Intervals & Checking Assumptions)
- Neural Networks
- How to Tune Neural Networks
- Types of Neural Networks
- Generative Adversarial Networks
This repo is updated every semester.