What Did we Learn and What to Expect in Assignment 4

Module Learning Outcomes

By the end of the module, students are expected to:

• Explain the notion of similarity-based algorithms.
• Broadly describe how 𝑘-NNs use distances.
• Describe the effect of using a small/large value of the hyperparameter 𝑘 when using the 𝑘-NN algorithm.
• Explain the problem of curse of dimensionality.
• Explain the general idea of SVMs with RBF kernel.
• Compare and contrast 𝑘-NNs and SVM RBFs.
• Broadly describe the relation of gamma and C hyperparameters with the fundamental tradeoff.

The assignment will concentrate on the learning objectives as well as building knowledge on existing concepts.

On to Assignment 4!