86 courses found
The DH Certificate Practicum provides principles for working with humanities materials as data, while maintaining a commitment to humanistic inquiry. Students will learn to integrate digital humanities methods into coursework and research required in their home departments. This course provides students with a broad understanding of basic technologies and approaches used by digital humanists and introduces data standards and data modeling.
Learning tools and concepts for computing on big data. Learn how to use Spark for large-scale analytics and machine learning. Spark is an open-source, general-purpose computing framework that is scalable and blazingly fast. Fundamental data types and concepts will be covered (e.g., resilient distributed datasets, DataFrames) along with Tools for data processing, storage, and retrieval, including Amazon Web Services (AWS).
Covers advanced theoretical concepts for deep neural networks. Topics include convolutional neural networks and their design principles, encoder-decoder architectures, recurrent neural networks, transformers, bounding box detection, image segmentation, generative adversarial networks, diffusion models, etc. Using open-source Python libraries such as NumPy, TensorFlow, and Keras, to understand how theoretical concepts are implemented.
Current state and future trends in Machine Learning Systems are covered. Topics include hardware systems, software systems, and Machine Learning optimized for metrics beyond predictive accuracy.
Advanced topics within Machine Learning.
This graduate-level course explores the principles, methodologies, and applications of human-centered design (HCD). Students will learn how to create solutions that meet real human needs. The course combines theoretical foundations with practical application through hands-on projects where students will define problems, prototype solutions, and test with users to develop impactful, user-centered products and services.
Introduces ways that data and information have historically been constructed in different realms--from medicine to public health to computing--to shed light on the power relationships embedded in some of our present-day and near-future tools, systems, and economic relationships. Will use a historical lens, as well as methods from STS, to give an introduction to how data and power interact in people's lives.
Transition into principal investigators and generators of data science-based knowledge. Develop practical skills necessary to conduct high quality data science research, advance development into producers and critical consumers of research, and further development into professional data scientists broadly defined. Research based career topics covered: time management, research products, types of research positions, and grant writing.
Networks provide a unifying framework to study the structure hidden within complex data. This graduate-level course focuses on the fundamental concepts and statistics as well as recent advancements and applications of network science. Topics include: graph theory, structural paradoxes, measures and algorithms for quantifying importance, community detection, network inference, recommendation systems, and link prediction.
Engages students in identification of a research question, a review of the literature and the application of an existing data science tool or technique (algorithm) to that problem. This is a mentored experience and will allow the student to demonstrate their capacity for research and begin to develop a relationship with a faculty mentor in Data Science. Course requires instructor permission.
PhD level Dissertation Research.