19: Course Summary

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