Data Science, Machine Learning Group

This group aims at learning and applying mathematical tools in the areas of Data Science and Machine Learning.

Objectives

1. Introduce the participant to inference problems and the process of decision making. Provide real-world examples and challenges.
2. Survey classical estimation and likelihood functions. Gain the ability to employ the maximum likelihood (ML) algorithm, the maximum a posteriori (MAP) estimator, the minimum mean square error (MMSE) estimator, and least mean squares (LMS) algorithm.
3. Understand iterative methods such as the expectation-maximization (EM) algorithm.
4. Provide an overview of Bayesian and graphical methods.
5. Explore fundamental concepts in high-dimensional estimation.
6. Carry performance analysis based on large sample sizes.
7. Engage the participant in an active learning experience. Provide an opportunity for the participant to conduct original research through small projects. Initiate the participant to team work and collaborative efforts.
8. Expose the participant to search engines, scholastic resources, research tools, indexes and databases. Prepare the participant to become an active contributor to the common body of knowledge. Encourage the participant to take part in open contests and shared content creation.