Lecture 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