. "Artificial Intelligence"@en . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . "Machine learning and the physical world"@en . . "5" . "Aims\r\n\r\nThe module “Machine Learning and the Physical World” is focused on machine learning systems that interact directly with the real world. Building artificial systems that interact with the physical world have significantly different challenges compared to the purely digital domain. In the real world data is scares, often uncertain and decisions can have costly and irreversible consequences. However, we also have the benefit of centuries of scientific knowledge that we can draw from. This module will provide the methodological background to machine learning applied in this scenario. We will study how we can build models with a principled treatment of uncertainty, allowing us to leverage prior knowledge and provide decisions that can be interrogated.\r\n\r\nThere are three principle points about machine learning in the real world that will concern us.\r\n\r\nWe often have a mechanistic understanding of the real world which we should be able to bootstrap to make decisions. For example, equations from physics or an understanding of economics.\r\nReal world decisions have consequences which may have costs, and often these cost functions need to be assimilated into our machine learning system.\r\nThe real world is surprising, it does things that you do not expect and accounting for these challenges requires us to build more robust and or interpretable systems.\r\nDecision making in the real world hasn’t begun only with the advent of machine learning technologies. There are other domains which take these areas seriously, physics, environmental scientists, econometricians, statisticians, operational researchers. This course identifies how machine learning can contribute and become a tool within these fields. It will equip you with an understanding of methodologies based on uncertainty and decision making functions for delivering on these challenges.\n\nOutcome:\nYou will gain detailed knowledge of\r\n\r\nsurrogate models and uncertainty\r\nsurrogate-based optimization\r\nsensitivity analysis\r\nexperimental design\r\nYou will gain knowledge of\r\n\r\ncounterfactual analysis\r\nsurrogate-based quadrature\r\n* Lectures take place in the Department of Computer Science and Technology and are part of the MPhil in Advanced Computer Science." . . "Presential"@en . "FALSE" . . "Master in Machine Learning and Machine Intelligence"@en . . "https://www.mlmi.eng.cam.ac.uk/" . "60"^^ . "Presential"@en . "The goal of the field of Machine Intelligence is to develop systems that can perceive the world, plan and make decisions, interact with humans and other intelligent agents, and provide explanations for their actions. Machine Learning provides many of the technical tools used to develop intelligent systems. This field overlaps with statistics and computer science.\r\n\r\nThe MPhil in Machine Learning and Machine Intelligence is an eleven month full-time programme offered by the Machine Learning Group, the Speech Group, and the Computer Vision and Robotics Group in the Cambridge University Department of Engineering. The course aims to teach the state-of-the-art in machine learning, speech and language processing, and computer vision; to give students the skills and expertise necessary to take leading roles in industry and to equip them with the research skills necessary for doctoral study at Cambridge and other universities.\n\nOutcome:\nThe MPhil in Machine Learning and Machine Intelligence offers many opportunities for the development of professional skills, giving students experience in preparing and giving presentations, report writing, collaborating in research teams, and carrying out literature searches. Students will also be expected to attend the invited seminar series run by the Machine Learning and the Speech Group."@en . . . . "1"@en . "FALSE" . . "Master"@en . "Thesis" . "17022.00" . "British Pound"@en . "41694.00" . "None" . "Employment prospects are also extremely good for students who plan to go directly into industry. The course will impart directly employable skills and expertise which are in great demand in the IT, financial, and manufacturing sectors."@en . "4"^^ . "TRUE" . "Midstream"@en . . . . . . . . . . . . . . . . . . .