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aabajianonJan 17, 2016

This really is a fantastic presentation for newcomers to the field. When I was taking these classes I found it difficult to keep all of the available algorithms organized in my mind. Here's an outline of his presentation:

Overview (5 slides)

General Concepts (9 slides)

K nearest Neighbor (6 slides)

Decision trees (6 slides)

K means (4 slides)

Gradient descent (2 slides)

Linear regression (9 slides)

Perceptron (6 slides)

Principal component analysis (6 slides)

Support vector machine (6 slides)

Bias and variance (4 slides)

Neural networks (6 slides)

Deep learning (15 slides)

I especially like the nonlinear SVM example on slides 57 and 58. It provides a visual of projecting data into a higher dimensional space.

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