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