1. Introduction and motivation 2. Linear algebra 3. Analytic geometry 4. Matrix decompositions 5. Vector calculus 6. Probability and distribution 7. Optimization 8. When models meet data 9. Linear regression 10. Dimensionality reduction with principal component analysis 11. Density estimation with Gaussian mixture models 12. Classification with support vector machines