Machine Learning with Small Data Part 2

By completing this course, you'll master building powerful machine learning systems that excel with limited data. You'll gain expertise in multi-task learning, meta-learning, and advanced data augmentation—from physics-based simulations to generative approaches—enabling models to adapt quickly and perform beyond their dataset size. What makes this course unique is its focus on cutting-edge 3D and generative technologies: Neural Radiance Fields (NeRF), diffusion models, and 3D Gaussian Splatting. Unlike traditional ML courses that assume abundant data, this program tackles real-world constraints while unlocking advanced capabilities in science, engineering, and creative industries. This course is primarily aimed at graduate students in computer science, engineering, or data science, along with industry professionals and researchers working with limited datasets who need to develop high-performance machine learning systems despite data constraints.