Recent years have witnessed increasing applications where data exhibits complex dependencies, including social networks, knowledge graphs, and biological data, etc. Machine learning and inference are vital techniques to make sense of this kind of data.
This workshop will bring two invited talks for the theme of learning and inference over complex data to our DICTA audiences. The first talk will cover how natural-language questions can be answered with the help of knowledge graphs, a powerful tool to represent complex relations among entities. The second talk will showcase how complicated statistical dependencies can be discovered from observational data via causal inference, a frontier of machine learning technique.
This workshop is targeted at professionals who would like to know the frontier of machine learning and inference for complex data. The workshop assumes background knowledge in supervised learning, basic statistical concepts and basic deep learning techniques.