How does Google decide what search results to present? How can a car camera identify automatically whether a pedestrian is crossing? How can we find out which drug is suitable for which patient? How can any application use data from many users to automatically improve user experience? These problems and many more can be solved with modern methods based on learning and analysis of big data. In this class we will survey diverse practical and successful methods, which can be implemented efficiently and simply, even with very large input sizes. We will understand the mathematical reasoning behind these methods, and apply them in practice on data from real problems. The following topics will be covered:
- Learning methods for automatic classification of objects to categories, such as SVM, kernels, decision trees, random forests and nearest neighbor.
- Condensed representation of big data using dimensionality reduction, such as PCA.
- Clustering methods such as k-means and spectral clustering
- Model fitting methods such as Gaussian mixtures, maximum likelihood, and EM.
This course is an elective course for bachelors/masters at Ben-Gurion University, Israel.