. "Neural machine translation and dialogue systems"@en . . "2" . "Aims\n\nThis half-module provides an introduction to machine translation and task-oriented dialogue systems as problems that can be addressed by machine learning. The presentation will employ sequence-to-sequence models to develop a uniform approach to these problems.\n\nOutcome:\nOn completion of this model, students should have a working familiarity with:\r\n\r\ntranslation and dialogue as problems in natural language processing;\r\ndata sets used in creating dialogue systems and machine translation systems;\r\nautomatic and manual assessment of dialogue and translation quality;\r\nthe statistical approach to task oriented dialogue systems and its component tasks;\r\nmodelling approaches for neural machine translation;\r\nsequence-to-sequence models, such as the Transformer architecture and instances such as GPT2\r\nfine tuning and domain adaptation procedures;\r\ncurrent research problems, including search and model correctness\r\ndata biases and ethical concerns in translation and dialogue." . . "Presential"@en . "TRUE" . . "Others"@en . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . "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 . . . . . . . . . . . . . . . . . . . .