Summary of course topics
- Supervised learning – labeled data
- Linear regression
- Logistic regression
- Neural networks
- Support vector machines
- Unsupervised learning – unlabeled data
- K-means
- PCA
- Anomaly detection
- Special applications/topics
- Recommender systems
- Large scale machine learning
- Advice on building machine learning systems
- Bias and variance
- Regularization
- What to do next when developing a system
- Algorithm evaluation
- Learning curves
- Error analysis
- Ceiling analysis
