Sahand Negahban

Sahand Negahban's picture
Assistant Professor of Statistics
24 Hillhouse Ave, New Haven, CT 06511-6814


STAT 365b / STAT 665b Data Mining and Machine Learning

Techniques for data mining and machine learning are covered from both a statistical and a computational perspective, including support vector machines, bagging, boosting, neural networks, and other nonlinear and nonparametric regression methods. The course will give the basic ideas and intuition behind these methods, a more formal understanding of how and why they work, and opportunities to experiment with machine learning algorithms and apply them to data. After STAT 242b.

Term: Spring
Day/Time: Monday & Wednesday, 2:30 - 3:45

STAT 679a High-Dimensional Statistical Estimation

In this course we will review the recent advances in high-dimensional statistics. We will cover concepts in empirical process theory, concentration of measure, and random matrix theory in the context of understanding the statistical properties of high-dimensional estimation methods. In this discussion we will also overview the computational constraints that are involved with solving high-dimensional problems and touch upon concepts in convex optimization and online learning.

Term: Fall
Day/Time: Tuesday, Thursday 12:00pm - 1:15pm