Mathematics for Foundations of Machine Learning & Deep Learning Courses

It is our mission to enable software developers & decision makers within every business to develop theoretical and practical understanding of machine learning & deep learning, including core principles and specialist topics. Artificial intelligence will revolutionise every industry and we want to enable you and your business to lead the way.FeedForward AI is a London-based AI consultancy that brings together cutting-edge research with practical applications of AI. Co-founded by Kevin Webster, Honorary Research Fellow in Mathematics at Imperial College London, we established the FeedForward AI Academy to make sure that businesses and individuals can develop vital skills in machine learning & deep learning.

FeedForward AI Academy

<H2>Course aims</H2>
<P>This 2-day course covers the mathematical background required to follow the Foundations of Machine Learning and Foundations of Deep Learning courses. It is aimed at people who would like to take one or both of these courses but do not feel secure enough in the mathematical prerequisites. 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 machine learning and deep learning algorithms covered in those courses.</P>
<H2>Summary of syllabus</H2>
<H3>Probability and statistics</H3>
<UL>
<LI>
<P>Probability theory. Sum and product rules of probability. Joint, conditional and marginal probability. Bayes’ theorem.</P>
</LI>
<LI>
<P>Discrete and continuous probability distributions. Probability densities, cumulative distribution function.</P>
</LI>
<LI>
<P>Expectation, variance and covariance of random variables.</P>
</LI>
<LI>
<P>Likelihood functions, prior and posterior distributions. Statistical estimators. Maximum likelihood estimation (MLE), maximum a posteriori (MAP) estimation.</P>
</LI>
<LI>
<P>Standard statistical distributions: binomial, Bernoulli, categorical/multinoulli, Gaussian, logistic etc.</P>
</LI>
</UL>
<H3>Linear algebra I</H3>
<UL>
<LI>
<P>Vectors and matrices. Matrix and vector operations.</P>
</LI>
<LI>
<P>Vector space, basis, linear independence. Change of basis.</P>
</LI>
<LI>
<P>Vector norms and inner products.</P>
</LI>
<LI>
<P>Orthogonality, orthonormal vectors. Orthogonal projections.</P>
</LI>
<LI>
<P>Gram-Schmidt procedure.</P>
</LI>
<LI>
<P>Linear transformations. Eigenvectors and eigenvalues.</P>
</LI>
<LI>
<P>Orthogonal matrices and rigid transformations.</P>
</LI>
<LI>
<P>Solving linear systems of equations. Matrix inverse and determinant.</P>
</LI>
<LI>
<P>Singular value decomposition.</P>
</LI>
</UL>
<H3>Calculus and optimisation</H3>
<UL>
<LI>
<P>Function derivatives. Interpretation as gradient or slope. Product rule and chain rule of differentiation.</P>
</LI>
<LI>
<P>Multivariate calculus, partial differentiation. Local optima.</P>
</LI>
<LI>
<P>Jacobian matrix and the Hessian.</P>
</LI>
<LI>
<P>Gradient descent.</P>
</LI>
</UL>
<H3>Information theory</H3>
<UL>
<LI>
<P>Information gain, bits and nats.</P>
</LI>
<LI>
<P>Entropy of a random variable. Average coding lengths.</P>
</LI>
<LI>
<P>Joint, conditional and cross entropy.</P>
</LI>
<LI>
<P>Relative entropy / Kullback-Leibler divergence and mutual information.</P>
</LI>
</UL>
<H2><SPAN>Tutor</SPAN></H2>
<P><SPAN>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.</SPAN></P>
<H2><SPAN>Who is this course for?</SPAN></H2>
<P>You might be a web developer, backend developer or data scientist interested in expanding your skills to include machine learning, and would benefit from studying or reviewing the core mathematical material required for either one of the Foundations courses. It will be assumed you have:<BR></P>
<UL>
<LI>
<P>Basic knowledge of the Python programming language.</P>
</LI>
<LI>
<P>Your own laptop to use during the session with the scientific computing libraries numpy and scipy pre-installed.</P>
</LI>
</UL>
<H2>Dates, times &amp; location</H2>
<P>Dates &amp; times: Two-day course running from Thu 31 Jan to Fri 1 Feb 2019, 09.30 - 16.30</P>
<P>Location: Central London, UK with easy access to a main tube station - the exact venue will be confirmed in the near future. </P>
<P>If you have any questions about the course, please email academy@feedforwardai.com.</P>

The Dock

Our {communityGuidelinesLink} describe the sort of content we prohibit on Eventbrite. If you suspect an event may be in violation, you can report it to us so we can investigate.

To report other categories of prohibited or illegal content, submit {link}

Mathematics for Foundations of Machine Learning & Deep Learning Courses

More events from FeedForward AI Academy

Discover more events from FeedForward AI Academy, from Science & Tech to other experiences you might love.

Still looking for the right event?

Explore all events in London and filter by date, category, and more to find the perfect fit.

Mathematics for Foundations of Machine Learning & Deep Learning Courses

More events from FeedForward AI Academy

Discover more events from FeedForward AI Academy, from Science & Tech to other experiences you might love.

More Science & Tech events

Browse more Science & Tech events with different dates, prices, and formats to find your next great experience.

Still looking for the right event?

Explore all events in London and filter by date, category, and more to find the perfect fit.