Week 19

19: Course Summary

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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
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