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MPhil in Machine Learning and Machine Intelligence


Applicants must also demonstrate compelling evidence of academic excellence or exceptional research ability.  Admission is extremely selective.  Suitable undergraduate degrees include Engineering or a related technical subject such as Computer Science, Physics, Chemistry or Mathematics.   A mathematically focussed Economics degree can sometimes be suitable preparation. Students will be expected to have strong backgrounds in mathematics and computer programming, as well as practical skills for large-scale experimentation.  Applications are welcomed from 'mature' students currently working in industry who have a UK first class honours degree or international equivalent. 

A typical candidate for this course is likely to have experience in the following areas:

  • Calculus and University-level Mathematics: differentiation, integration, vector calculus, ODEs/PDEs, Fourier series, vector gradients, coordinate systems, etc.
  • Linear algebra: vectors, matrices, linear transformations, matrix inversion, eigenvalues and eigenvectors, matrix factorization, SVD, least squares solutions, etc.
  • Probability and Statistics: random variables, random processes, expectation, mean and variance, independence and conditional probability, law of large numbers, stationarity, correlation, Markov chains, central limit theorem, etc.
  • Inference: maximum likelihood and Bayesian estimation, regression, classification, clustering, Markov models and Hidden Markov models, Monte Carlo, etc.