This course focuses on recent development of machine learning techniques that are promising for solving practical problems in image/video indexing, recognition, comprehension and deep learning. Especially, we will focus on the emerging needs and solutions in very large-scale and diverse media types, sensors, and in-depth deep learning neural networks. The goal is for students to get familiar with the state of the art, learn how to formulate and solve practical image/video learning/indexing, and acquire hands-on experiences through actual experiments. The course will include some topics in depth such as:

- Data augmentation strategies for deep neural networks
- Advanced Face Recognition and Beyond
- Advanced hash learning and optimization
- Manifold learning
- Sparse coding and solvers
- Latent semantic analysis
- Automatic neural network learning (autoML) + structure learning
- Cross-domain (transfer) learning
- Deep comprehension and question and answering
- Efficient computation for neural networks
- Few (zero) shot learning
- Reinforcement learning from sensors and data
- Experience sharing from the domain experts

See more details at the course webpage:
https://winstonhsu.info/ammai-19s/