Machine Learning and Machine Intelligence University of Cambridge

Overview

Machine Learning and Machine Intelligence from University of Cambridge course is split into five specialised pathways, which define the area in which the dissertation will fall, and which each have different compulsory and permissible module combinations. 

The five pathways are: (i) Machine Learning; (ii) Speech and Language Processing; (iii) Computer Vision and Robotics; (iv) Human-Computer Interaction; (v) Biological Learning. The course aims to teach the state of the art in machine learning and machine intelligence; to give students the skills and expertise necessary to take leading roles in industry; and to equip students with the research skills necessary for doctoral study.

By the end of the programme, students will have acquired:

• knowledge of the fundamental techniques in machine learning and how to apply these techniques to a range of practical problems;

• a deep understanding of fundamental problems in machine intelligence, including speech and language processing and computer vision, and the technologies that form the current state of the art;

• a comprehensive understanding of techniques, and a thorough knowledge of the literature, applicable to the area of their chosen research topic;

• presentation skills through presenting their research in progress;

• the methodological and other technical skills necessary for research in their chosen area;

• the ability to critically assess the technical literature in machine learning and machine intelligence and related topics;

• directly marketable skills in computing, machine intelligence, machine learning, and the data sciences;

• collaborative skills through working with other students on the practical exercises and with PhD students and research assistants while carrying out their research project;

• experience in large-scale computing for machine learning and machine intelligence; and

• an understanding of how to define and conduct a research project.