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Classifiers are a very popular branch of machine learning, with myriad practical applications

Subject Matter Expert[edit]



  1. Discuss classifiers, including their inputs, outputs
  2. Describe the strengths and weaknesses of classifiers
  3. Demonstrate an understanding of Naive Bayes classifiers
    1. Describe the idea of conditional probability
    2. Demonstrate the derivation of Bayes's Theorem
    3. Explain how Bayes's Theorem is applied to create a Bayesian classifier
    4. Demonstrate the creation of data structures appropriate for Naive Bayesian classification given a small sample dataset
  4. Demonstrate an understanding of Support Vector Machines
    1. Discuss the idea of higher-dimensional feature spaces
    2. Discuss separability and the challenges it poses
    3. Discuss separating planes, both verbally and graphically
    4. Explain the idea and motivation of a maximum margin hyperplane
    5. Discuss the kernel trick
  5. Demonstrate practical knowledge. The student will provide a training set and a test set. Then, using one of the above techniques, and training only on the training set, they must achieve 80+% accuracy on the test set.


Bayes Resources[edit]

SVM Resources[edit]