Lecture 20 | Machine Learning

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Topics in this lecture:
Comparing classifiers
No free lunch theorem
Assumptions, assumptions, assumptions (or hypotheses)
A look back at "PCA versus LDA"
Ugly duckling theorem
Minimum description length
Overfitting
Bias and variance of the error function
Resampling in estimation
Leave-one-sample-out
Bootstrap
Cross-validation and m-fold cross-validation
Regression models with cross-validation
Regression models and the bias versus variance decomposition
Stability of a classifier
Generalization
VC dimension (Vapnik–Chervonenkis dimension)
VC in Empirical Risk Minimization (ERM) algorithms
Stability in non-ERM algorithms

  1. CBCSL teaching
  2. machine learning
  3. pattern recognition
  4. computer science
  5. statistics
  6. engineering
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