Overview

This is a hands-on class surveying a range of mathematical methods, models, and concepts used in machine learning.  An understanding of basic linear algebra and probability is needed, as well as programming skills. Python will be used.

Slides

1. Introduction and Perceptron
   • code
2. Python tools for ML
   • scikit-learn exercise
3. Linear algebra Review
   • dim reduction exercise
4. Linear algebra review, part II
5. Curse of dimensionality
6. k nearest neighbor classification
7. Statistics for ML
8. Probability and Naive Bayes
9. Model optimization and regularization
10. Bias, variance, and metrics
11. Support vector machines
12. Neural Networks
13. Deep Learning with Keras