Machine learning methodology is primarily concerned with designing appropriate models/algorithms for datasets and problems, plus the capacity to learn the model parameters given data (made more complex with “big data”). Machine-learning has a broad range of applications, from making improved diagnoses in health care to tailoring products and ads to individual customers. There are many applications of machine learning in health, science, business, engineering, social science, and the humanities. With increasing access to massive datasets, and to significant advances in computing resources, the quality of machine learning performance (e.g., prediction accuracy) has improved markedly.
The Duke Machine Learning Summer School will concentrate on methods that allow machine-learning algorithms to learn effectively on large datasets.
Throughout the course, participants will:
- Understand and leverage deep machine learning
- Learn the latest methods for image and video analysis, natural language processing, reinforcement learning, and data synthesis/modeling
- Explore the mathematical and statistical principles that lie at the heart of machine learning
- Receive hands-on training with software using the Google TensorFlow platform
Who Should Attend?
This program is most appropriate for individuals (students, faculty and staff) interested in learning about machine learning, with a focus on recent algorithms, like deep learning. Participants will learn the mathematics and statistics at the foundation of modern machine learning and get hands-on training in the latest machine learning software, using Google TensorFlow platform.
Participants should have a strong background in computing (e.g., with Python), to be capable of learning how to use and apply modern machine learning software. For participants who also have a strong mathematical and statistical background (strength in calculus and in basic statistics, at the senior undergraduate level), the opportunity to understand the fundamentals of machine learning will be available. Strength in mathematics and statistics is a significant plus; however, it is not required to benefit from the hands-on software portion of the program.