£274.80 – £478.80

Mathematics for the Foundations of Machine Learning & Deep Learning, evenin...

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Course aims

This course, taught in person over 4 weekly evenings, covers the mathematics required for understanding the fundamentals of machine learning and deep learning. By the end of the course you will have a good understanding of several core mathematical concepts, and the tools required to understand the theory behind the some of the most important machine learning and deep learning algorithms.

This is an intensive course ideally suited to people already in technical or quantitative roles - you might be a data scientist or software developer who wants to deepen their knowledge of machine learning.

Summary of syllabus

Probability and statistics

  • Probability theory. Sum and product rules of probability. Joint, conditional and marginal probability. Bayes’ theorem.

  • Discrete and continuous probability distributions. Probability densities, cumulative distribution function.

  • Expectation, variance and covariance of random variables.

  • Likelihood functions, prior and posterior distributions. Statistical estimators. Maximum likelihood estimation (MLE), maximum a posteriori (MAP) estimation.

  • Standard statistical distributions: binomial, Bernoulli, categorical/multinoulli, Gaussian, logistic etc.

Linear algebra I

  • Vectors and matrices. Matrix and vector operations.

  • Vector space, basis, linear independence. Change of basis.

  • Vector norms and inner products.

  • Orthogonality, orthonormal vectors. Orthogonal projections.

  • Gram-Schmidt procedure.

  • Linear transformations. Eigenvectors and eigenvalues.

  • Orthogonal matrices and rigid transformations.

  • Solving linear systems of equations. Matrix inverse and determinant.

  • Singular value decomposition.

Calculus and optimisation

  • Function derivatives. Interpretation as gradient or slope. Product rule and chain rule of differentiation.

  • Multivariate calculus, partial differentiation. Local optima.

  • Jacobian matrix and the Hessian.

  • Gradient descent.

Information theory

  • Information gain, bits and nats.

  • Entropy of a random variable. Average coding lengths.

  • Joint, conditional and cross entropy.

  • Relative entropy / Kullback-Leibler divergence and mutual information.


The FeedForward AI Academy programmes are led by Dr Kevin Webster, Honorary Research Fellow in Mathematics at Imperial College. Kevin recently completed teaching the graduate level course on Deep Learning in the mathematics department at Imperial College London in Autumn 2018.

Who is this course for?

You might be a web developer, backend developer or data scientist interested in expanding your skills to include machine learning or deepening your current knowledge by studying or reviewing the core mathematical material. It will be assumed you have:

  • Basic knowledge of the Python programming language.

  • Your own laptop to use during the session with the scientific computing libraries numpy and scipy pre-installed.

Dates, times & location

Dates & times: 4 evenings course running from Tue 30 April to Tue 21 May 2019, 18.00 - 21.00.

Location: Central London, UK with easy access to a main tube station - the exact venue will be confirmed in the near future.

If you have any questions about the course, please email academy@feedforwardai.com.

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