+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
Wednesday, February 5 - 4:30pm to 6:30pm
Location: Bostock Library, 1st floor, Edge Workshop
Instructor: Qiang Qiu

The basic concepts of neural networks are introduced, with a focus on intuition. The simpler and widely used logistic regression model is introduced first, and from this the neural network is introduced as a generalization.

Wednesday, February 12 - 4:30pm to 6:30pm
Location: Bostock Library, 1st floor, Edge Workshop
Instructor: David Carlson

In machine learning, models are developed to represent and make predictions based on data. The model starts with random parameters and must “learn” these parameters by using historical data.

Thursday, February 13 - 4:30pm to 6:30pm
Location: Bostock Library, 1st floor, Edge Workshop
Instructor: Jessilyn Dunn

Recent technological advancements make it possible to closely and continuously monitor patients on multiple scales, both inside and outside of the clinic.

Wednesday, February 19 - 4:30pm to 6:30pm
Location: Bostock Library, 1st floor, Edge Workshop
Instructor: Kevin Liang

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. Bring a laptop and be ready to code!

Thursday, February 20 - 4:30pm to 6:30pm
Location: Bostock Library, 1st floor, Edge Workshop
Instructor: Lawrence Carin

Natural language processing (NLP) is a field focused on developing automated methods for analyzing text, and also for computer-driven text generation (synthesis, for example in translation). Neural networks have recently become the state-of-the-art method for NLP.

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