ML Coursework

Spring 2022

Exploratory Text Analytics

Introduction to text analytics with a focus on long-form documents, such as reviews, news articles, and novels. Students convert source texts into structure-preserving analytical form and then apply information theory, NLP tools, and vector-based methods to explore language models, topic models, sentiment analyses, and narrative structures. The focus is on unsupervised methods to explore cognitive and social patterns in texts.

Deep Learning

This course will focus on Spark, an open-source, general-purpose computing framework that is scalable & fast. Fundamental data types & concepts are covered. You will learn how to use Spark for large-scale analytics & machine learning, among other topics. Tools for data storage and retrieval are covered, including AWS.

Big Data Systems

A graduate-level course on deep learning fundamentals and applications with emphasis on their broad applicability to problems across a range of disciplines. Topics include regularization, optimization, convolutional networks, sequence modeling, generative learning, instance-based learning, and deep reinforcement learning.

Data Visualization (Audit)

Thinking with Images. People have been looking at data for centuries -- with their eyes -- to discover patterns, meaning, and insight into the most important challenges of their time. This course teaches visual and spatial thinking coupled with visual data tools and interactive web coding to envision information. Far beyond plotting, finding ways to respond to complex problems, we will study and make useful, compelling, and beautiful tools to see.

Fall 2021

Foundations of Computer Science

Provide a foundation in discrete mathematics, data structures, algorithmic design and implementation, computational complexity, parallel computing, and data integrity and consistency for non-CS, non-CpE students. Case studies and exercises will be drawn from real-world examples (e.g., bioinformatics, public health, marketing, and security).

Ethics of Big Data

This course examines the ethical issues arising around big data and provides frameworks, context, concepts, and theories to help students think through and deal with the issues as they encounter them in their professional lives.

Statistical Learning

This course covers fundamentals of data mining and machine learning within a common statistical framework. Topics include regression, classification, clustering, resampling, regularization, tree-based methods, ensembles, boosting, and Support Vector Machines.

Bayesian Machine Learning

Bayesian inferential methods provide a foundation for machine learning under conditions of uncertainty. Bayesian machine learning techniques can help us to more effectively address the limits to our understanding of world problems. This class covers the major related techniques, including Bayesian inference, conjugate prior probabilities, naive Bayes classifiers, expectation maximization, Markov chain monte carlo, and variational inference.

Summer 2021

Linear Models for Data Science

An introduction to linear statistical models in the context of data science. Topics include simple and multiple linear regression, generalized linear models, time series, analysis of covariance, tree-based classification, and principal components.

Programming for Data Science

An introduction to essential programming concepts, structures, and techniques. Students will gain confidence in not only reading code, but learning what it means to write good quality code. Additionally, essential and complementary topics are taught, such as testing and debugging, exception handling, and an introduction to visualization.

Practice and Applications of Data Science