19: Course Summary

[ 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

 

SummaryOfML

ML

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Author: iotmaker

I am interested in IoT, robot, figures & leadership. Also, I have spent almost every day of the past 15 years making robots or electronic inventions or computer programs.

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