+DS provides training modules and learning experiences grounded in generalizable data science content, while partnering with individual units or groups to develop additional specialized content.

Online Learning

Coursera Online Modules

The online +DS modules introduce the basics of data science, across multiple important application domains. These online modules are used as prerequisites for the in-person learning experiences listed below. Together, the online content supports the in-person "flipped" learning experiences. 

Module 1: Introduction to Machine Learning

Logistic regression, a simple machine learning (ML) method, is introduced and then extended to the multilayered perceptron (MLP), a fundamental neural network. Conceptual understanding is provided to motivate the form of the MLP, and its use.

Module 2: Basics of Model Learning

Concepts in learning model parameters are introduced, as well as model validation and testing. Learning based on stochastic gradient descent is addressed, allowing scaling to large datasets ("big data").

Module 3: Image Analysis and the Convolutional Neural Network

The convolutional neural network (CNN) is developed for image analysis, including details of the model and its underlying components. Model training is covered, as well as transfer learning and fine-tuning.

Module 4: Introduction to Natural Language Processing

Application of neural networks to natural language processing (NLP) is covered, from simple neural models to the more complex. The fundamental concept of word embeddings is discussed, as well as how such methods are employed within model learning and usage for several NLP applications.

Module 5: Introduction to Reinforcement Learning

Reinforcement Learning is introduced, a fundamental concept in machine learning that is concerned with taking suitable actions to maximize rewards in a particular situation. After learning the initial steps of Reinforcement Learning, the module addresses Q Learning as well as Deep Q Learning, and discusses the difference between the concepts of Exploration and Exploitation and why they are important.

Please note that Coursera for Duke is accessible to only Duke students, faculty, and staff. If you are not a member of the Duke community, you can access the public version of this Coursera course: https://www.coursera.org/duke.

Recorded Content from Machine Learning Summer School

In addition to the aforementioned Coursera content, +DS offers recordings of Duke’s Machine Learning Summer School (MLSS), which was help in June of 2018. If you are a Duke student, staff or faculty member, you can review these classroom recordings on Panopto, with accompanying slides and links to github code demos.

In-Person Learning Experience

In addition to the online content, +DS offers in-person opportunities to dive deeper into the information introduced in the online modules. These learning experiences will be developed to target diverse units at Duke: from those that desire a broad understanding of what is possible with data science, and those who wish to use data-science tools (software) without a need for deep understanding of underlying methodology, to those who desire a rigorous technical proficiency of the details and methodology of data science.

See past learning experiences

Upcoming In-Person Learning Experiences
Tuesday, March 31 - 4:30pm to 6:30pm
Location: Virtual, Classroom
Instructor: Matthew Kenney

AI is playing an increasingly large role in the Digital Humanities. The use of AI throughout the humanities can accelerate research, open up new forms of investigation, and create novel approaches to interacting with data.

Wednesday, April 1 - 4:30pm to 6:30pm
Location: Virtual, Classroom
Instructor: Timothy Dunn

The convolutional neural network (CNN) represents the current state-of-the-art for image and video analysis, and is increasingly used for analyzing time series and other data with spatial or sequential structure.

Thursday, April 2 - 4:30pm to 6:30pm
Location: Virtual, Classroom
Instructor: Serge Assaad

PyTorch is an open source machine learning framework popular for building neural networks. In this hands-on session, we'll walk through building and training a neural network, introducing the basic mechanics of PyTorch.

Wednesday, April 8 - 4:30pm to 6:30pm
Location: Virtual, Classroom
Instructor: Ricardo Henao

Omics aims to understand biological processes by leveraging high-throughput technologies and data science. Aided by subject matter expertise, this combination has resulted in accelerated discoveries in health and disease.

Wednesday, April 8 - 4:30pm to 6:30pm
Location: Virtual, Classroom
Instructor: Matthew Hirschey

The ability to make data-driven decisions is redefining the future of patient care.

Thursday, April 9 - 4:30pm to 6:30pm
Location: Virtual, Classroom
Instructor: Matthew Hirschey

The ability to make data-driven decisions is redefining the future of patient care.

Thursday, April 16 - 4:30pm to 6:30pm
Location: Virtual, Classroom
Instructor: Andrew Michael

This training will consist of two main sections: (1) application of ML to brain images from a clinical archive to detect brain disorders and (2) extraction of brain features from a large publicly available dataset to better understand mental health.

Recorded Content from Lunch and Learns

+DS offers recordings from a series of lunch and learn sessions held in fall 2019 about how artificial intelligence (AI) is transforming healthcare. Both clinical experts and technical experts led each session, with content split roughly 50/50 between the clinical setup and technical approach, and a focus to share both the medical context and the applications of data science for health.

Digital Pathology: Identifying Thyroid Malignancy with Deep Learning

  • Danielle Range, MD, Assistant Professor of Pathology
  • Yoni Cohen, MD, Medical Instructor, Department of Head and Neck Surgery & Communication Sciences
  • Ricardo Henao, PhD, Assistant Professor of Biostatistics and Bioinformatics; Principal Data Scientist, Duke Forge
  • View the video of this session (requires Duke login): https://duke.mediasite.com/Mediasite/Play/a97f106d5dc846468e65fe274730f4bf1d

A Window to the Brain: Analysis of Retinal Images with Deep Neural Networks

  • Sharon Fekrat, MD, Professor of Ophthalmology; Associate Professor, Department of Surgery
  • Felipe Medeiros, MD, PhD, Joseph A.C. Wadsworth Professor of Ophthalmology
  • Dilraj Singh Grewal, MBBS, Associate Professor of Ophthalmology
  • Lawrence Carin, PhD, James L. Meriam Professor of Electrical and Computer Engineering; Vice President for Research, Duke University
  • View the video of this session (requires Duke login): https://duke.mediasite.com/Mediasite/Play/044abb60a25d4eea9705aa51343af36b1d

Early Autism Screening with Machine Learning

  • Geraldine Dawson, PhD, William Cleland Professor of Psychiatry & Behavioral Sciences; Director, Duke Center for Autism and Brain Development; Director, Duke Institute for Brain Sciences
  • Guillermo Sapiro, PhD, James B. Duke Professor of Electrical and Computer Engineering; Professor of Mathematics
  • View the video of this session (requires Duke login): https://duke.mediasite.com/Mediasite/Play/588abd8d0e8148aba7b8ea27dd82e9721d

Recommending MyChart Responses with Natural Language Processing