Teaching

Spring 2023, 2024, 2025

EN.580.464/664 Advanced Data Science for Biomedical Engineering

This course will focus on the fundamental concepts, learning paradigms, and problems in modern data science. It will provide students with a precise characterization of problems and algorithms for learning from data, from problem formulations to data models to algorithms. We will cover supervised and unsupervised problems, as well as topics on modern aspects of the application of data science in the 21st century, including aspects of interpretability and fairness. This course is "advanced" in the sense that it will presume undergraduate-level courses in statistics, linear algebra, programming, and basic introductory classes to data science. It is ideal for undergraduate students wanting to specialize in data science, or for graduate students looking for a theory-oriented introduction on data science.

The site of this class is available on CANVAS.

Spring 2022

EN.500.115  Gateway Data Science

This course introduces fundamental data science concepts and techniques. It is intended for all who plan work on data driven projects, and will serve as a prerequisite for advanced courses in data science and machine learning. Topics covered include linear and nonlinear regression, classification, clustering, and dimensionality reduction. Students deploy Python packages on data sets and apply data science methods on engineering and science problems. Course homework involves significant programming. Attendance and participation in class sessions are expected.

The site of this class is available on CANVAS.

Fall 2019, 2020, 2021

EN.580.709  Sparse Representations in Computer Vision and Machine Learning

This course focuses on sparsity as a model for general data, generalizing many different other constructions or priors. This idea - that signals can be represented with just a few coefficients - leads to a long series of beautiful (and surprisingly, solvable) theoretical and numerical problems, and many applications that can benefit directly from the newly developed theory. This course surveys the field starting with the theoretical foundations and systematically making our way the results gathered in the past years. This course will touch on theory, numerical algorithms, and applications in image processing and machine learning. Recommended course background: Linear Algebra, Signals and Systems, Numerical Analysis.

Old site from the 2019 class here.

Mondays 3:00PM - 5:00PM