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Foundations of Machine Learning, 3-day course

Course aims

This 3-day course aims to give developers/technical personnel a solid grounding in the field of machine learning, assuming no prior knowledge of the field. Regular practical sessions are combined with lecture-style content to consolidate the course material. By the end of the course you will understand the concepts underlying many popular machine learning algorithms, how to fit the models and where to apply them. You will also practice implementing some of these algorithms, and gain experience working with the popular python machine learning library scikit-learn.

Summary of syllabus

  • Introduction to artificial intelligence (AI) and machine learning (ML). Basic important concepts underpinning machine learning.

  • Mathematical prerequisites review.

  • Linear regression. Basis functions, numerical and analytical solutions, regularisation.

  • Classification algorithms. K-nearest neighbours, logistic regression, neural networks, naive Bayes classifier.

  • Dimensionality reduction. Principal components analysis (PCA). Nonnegative matrix factorisation (NMF).

  • Clustering algorithms. K-means clustering.

  • Support vector machines. Maximum margin classifiers, linear and nonlinear models.

  • Anomaly detection algorithms. Density based models, k-NN, local outlier factor. One class SVMs, elliptic envelope, isolation forest.


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. This course presumes pre-existing technical proficiency.

It will be assumed you have:

  • Basic knowledge of Git, Object Oriented Programming and the Python programming language

  • Your own laptop to use during the session with scikit-learn, numpy and scipy pre-installed. We recommend anaconda distribution for ease of installation.

  • Understanding of core mathematical concepts as listed in Mathematics for Foundations of Machine Learning and Deep Learning Courses. These topics will be briefly refreshed but not taught from scratch. If you are not confident with these topics, we suggest you take the mathematics course first.

Dates, times & location

Dates & times: Three-day course running from Wed 6 to Fri 8 Feb 2019, 09.30 - 16.30.

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 

Tickets can be purchased through Eventbrite using the button below.