THEORY AND ALGORITHMS FOR Machine Learning
Instructor: Prof. Cynthia Rudin
This is an introductory overview course at an advanced level. It covers standard techniques, such as the perceptron algorithm, decision trees, random forests, boosting, support vector machines and reproducing kernel Hilbert spaces, regression, K-means, Gaussian mixture models and EM, neural networks, and multi-armed bandits.
General topics include:
- Basic machine learning evaluation techniques, including ROC curves and cross validation
- Top 10 algorithms in data mining (including optimization and ensemble methods)
- Statistical learning theory
- Introductory online learning - mistake bounds, multi-armed bandits
- Bayesian Methods in ML (Gaussian mixture models, Bayesian and frequentist interpretations)
Fluency with basic skills such as linear algebra, analysis (including proofs), probability (advanced), and programming.