Machine Learning systems Design
Machine learning solutions, in particular those based on deep learning methods, form an underpinning of the current revolution in “artificial intelligence” that has dominated popular press headlines and is having a significant influence on the wider tech agenda. In this talk I will give an overview of where we are now with machine learning solutions, and what challenges we face both in the near and far future. These include practical application of existing algorithms in the face of the need to explain decision-making, mechanisms for improving the quality and availability of data, dealing with large unstructured datasets.
Neil Lawrence is a Professor of Machine Learning at the University of Sheffield. His main technical research interest is machine learning through probabilistic models. He focuses on both the algorithmic side of these models and their application. He has a particular interest on applications in personalized health and applications in the developing world. Neil is well known for his work with Gaussian processes, and has proposed Gaussian process variants of many of the successful deep learning architectures. He is also an advocate of of the ideas behind “Open Data Science” and active in public awareness (see https://www.theguardian.com/profile/neil-lawrence) and community organization. He has been both program chair and general chair of the NIPS Conference.