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Registration Open for 2022 Duke Machine Learning Winter School: Computer Vision

The Duke+Data Science program is pleased to announce the Duke Machine Learning Winter School: Computer Vision (MLWS-CV), being offered in January 2022 as a live, virtual three-day class that provides lectures on the fundamentals of machine learning and computer vision.

The curriculum in the MLWS-CV is targeted to individuals interested in learning about machine learning (ML) with a focus on computer vision: a field that seeks to develop foundational theory and computational approaches for the characterization and understanding of digital images. From an applied perspective, computer vision aims at using such approaches to automate tasks traditionally carried out by humans. The MLWS-CV will introduce the mathematics and statistics at the foundation of modern machine learning models for computer vision and provide context for the methods that have formed the foundations of rapid growth in artificial intelligence (AI). Additionally, the MLWS-CV will provide hands-on training in the latest machine learning software, using the widely used (and free) PyTorch framework.

Nine prior Duke Machine Learning Schools have been presented since 2017, reaching hundreds of participants from academia and industry and including international audiences at the SingHealth/Duke NUS Medical School and the Duke Kunshan University campus. Last year’s machine learning school attracted 170 participants from around the world, representing 43 universities, institutes, and corporations.

The 2022 MLWS-CV will be led by a trio of machine learning experts at Duke University: Professors Ricardo Henao, David Carlson, and Timothy Dunn, joined with lectures and hands-on coding sessions by other Duke experts. Teaching assistants will be available for assistance throughout the course.

Who Should Attend

The MLWS-CV is particularly well-suited to members of academia and industry, including students and trainees, who seek a thorough introduction to the methods of machine learning, including interpretation and commentary by respected leaders in the field.

The MLWS-CV is meant to provide value to students at multiple levels of mathematical sophistication (including with limited such background). On each day, an initial emphasis will be placed on presenting the concepts as intuitively as possible, with minimum math and technical details. As the concepts are developed further, more math will be introduced, but only the minimum necessary to explain the concepts. Then case studies will show how the technology is used in practical computer vision applications, and these discussions should be accessible to most students (concepts emphasized over detailed math). Strength in mathematics and statistics is a significant plus, and will make all MLWS-CV material more accessible; however, it is not required to benefit from much of the program. Finally, the class will also introduce participants to the coding software used to make such technology work in practice.

To register or to apply for a scholarship, please go to


The broad areas of emphasis for the three day class are as follows:

Sunday, January 2, 2022 (9:00 AM – 2:30 PM Eastern Time):

  • Basic concepts in machine learning
  • Introduction to model building and the multi-layered perceptron (MLP)
  • Scaling to “big data” with stochastic gradient descent
  • Backpropagation as an efficient computation method
  • Coding session covering introductory concepts of ML concepts

Monday, January 3, 2022 (9:00 AM – 4:00 PM):

  • Image analysis with convolutional neural networks (CNNs)
  • Deep convolutional neural networks
  • Image segmentation, object detection, and object localization
  • Coding session covering image classification with CNN models
  • Case study in volumetric medical image analysis and interpretation with CNN models

Tuesday, January 4, 2022 (9:00 AM – 4:00 PM):

  • Image synthesis with generative models
  • Generative adversarial networks (GANs)
  • Conditional image generation and translation
  • Coding session covering image segmentation and object detection
  • Case study in ethical issues around computer vision, including facial recognition

Teaching assistants from the Duke AI Health Fellowship program will be present throughout the program to support the virtual format and will be easily available for assistance and consultation.

Program Format

The 3-day class will provide lectures on the mathematics and statistics at the heart of machine learning with a special emphasis on models and applications in computer vision, plus hands-on training on implementing machine learning tools with the PyTorch software platform, and case studies of the methods applied to specific application areas.

Each day of the MLWS-CV will be arranged as follows (Eastern Time):

  • 9:00-10:15am   Lecture 1: Mathematically-light introduction to the focus of the day
  • 10:45am-noon  Lecture 2: Mathematically rigorous discussion of the focus of the day
  • 1:00-2:30pm     Software discussion and hands-on training with PyTorch
  • 3:00-4:00pm     Case Study of machine learning in practice (for Monday and Tuesday)

At the end of the MLWS-CV, each student will have a deeper understanding of the fundamental concepts of machine learning and applied computer vision, including context for the rapidly evolving field of artificial intelligence. For those students with sufficient mathematical background, the underlying methodology of machine learning will also be learned. Each student should be able to utilize PyTorch to implement the latest machine learning methods for analysis of images, video, and natural language (text).

Program Details: Location, Registration and Cost

Students (with a valid ID, at Duke or other universities) will pay a course fee of $40; the fee for non-students is $160, payable through the registration site. All fees are non-refundable. Once we reach maximum registration, we will maintain a waitlist, and will contact those on the waitlist as spots become available. We also have a small number of scholarships available for those who would be otherwise unable to join.

Each participant will receive a personal link for the virtual webinars, which will be held live and provide opportunities for questions and engagement with each lecturer. We strongly encourage live participation, but every participant will also have access to the video recordings to use for their personal reference.

Relevance and Context

Machine learning is a field characterized by development of algorithms that are implemented in software and run on a machine (e.g., computer, mobile device, etc.). Each such algorithm is characterized by a set of parameters, and particular parameter settings yield associated algorithm characteristics. The algorithms have the capacity to learn, based on observed data. By “learn” it is meant that the algorithm can infer (or learn) which algorithm parameter settings are best matched to the data of interest. After algorithm parameters are so learned, the associated model ideally captures the underlying characteristics of the data. The algorithm, with learned parameters, may subsequently be applied to new data, with the goal of making predictions or learning insights. Machine learning methodology is primarily concerned with designing appropriate models/algorithms for datasets and problems of interest, plus the capacity to learn the model parameters given data (with challenges manifested when that data is of a massive scale).

In the context of prediction, one may be interested in developing algorithms that are capable of automatically classifying and interpreting imaging data. For instance, in a healthcare setting, to improve clinical care. In this case, the healthcare data may be radiological images (e.g., x-rays, ultrasound videos, computed tomography volumes, etc.) and/or a history of patient care. In healthcare, the goal is to use machine learning to make improved diagnoses, interpretation (e.g., location of abnormalities within an image) and recommendations for care. Similar concepts are of interest in business, where one may be interested in tailoring advertising and products to individuals or improving image search. In education, machine learning may be used to tailor educational material to the level and interests of each student. Machine learning is increasingly making an impact in almost all areas of personal and professional life.

Recently, with increasing access to massive imaging datasets (e.g., ImageNet), and to significant advances in computing resources, the quality of machine learning performance (e.g., prediction accuracy) has improved markedly. Further, over the last five years, significant advances have been made in a subfield of machine learning called “deep learning” that have completely changed the landscape and boundaries of computer vision to the point in which computer vision models, in multiple scenarios, have surpassed human ability in image classification and object detection tasks, as well as being able to generate images of such quality that are indistinguishable from real images by most humans.

This class will focus on the areas of machine learning that have made the biggest advances in utility over the last several years, including deep learning. The class will concentrate on methods that allow machine-learning algorithms to train effectively on massive datasets, i.e., “big data”.

The 2022 MLWS-CV is presented by the Duke+Data Science (+DS) program, which is one of the partner programs supporting the mission of the Duke Center for Computational Thinking (CCT). If you have any questions, please send an e-mail to