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Machine Learning:  2023-2024



Schedule B1 (CS&P)Computer Science and Philosophy

Schedule A2Computer Science

Schedule B1Computer Science

Schedule A2(M&CS)Mathematics and Computer Science

Schedule B1(M&CS)Mathematics and Computer Science

Michaelmas TermMSc in Advanced Computer Science



Machine learning techniques enable us to automatically extract features from data so as to solve predictive tasks, such as speech recognition, object recognition, machine translation, question-answering, anomaly detection, medical diagnosis and prognosis, automatic algorithm configuration, personalisation, robot control, time series forecasting, and much more. Learning systems adapt so that they can efficiently solve new tasks related to previously encountered tasks.

This course will introduce the field of machine learning, in particular focusing on the core concepts of supervised and unsupervised learning. In supervised learning we will discuss algorithms which are trained on input data labelled with a desired output, for instance an image of a face and the name of the person whose face it is, and learn a function mapping from the input to the output. Unsupervised learning aims to discover latent structure in an input signal where no output labels are available, an example of which is grouping news articles based on the topics they cover. Students will learn algorithms which underpin many popular machine learning techniques, as well as developing an understanding of the theoretical relationships between these algorithms. The practicals will concern the application of machine learning to a range of real-world problems.

More information:

For detailed and up-to-date information, visit the Moodle page for this course.



Machine learning is a mathematical discipline and it requires a good background in linear algebra, calculus, probability and algorithms. If you have not taken the following courses (or their equivalents) you should speak with the lecturers prior to registering for this course.


  • Introduction to different paradigms of machine learning
  • Linear prediction, Regression
  • Maximum Likelihood, MAP, Bayesian ML
  • Regularization, Generalization, Cross Validation
  • Basics of Optimization
  • Linear Classification, Logistic Regression, Naïve Bayes
  • Support Vector Machines
  • Kernel Methods
  • Neural Networks, Backpropagation
  • Convolutional Neural Networks
  • Recurrent Neural Networks
  • Unsupervised Learning, Clustering, k-means
  • Dimensionality Reduction, PCA

Reading list

  • C. M. Bishop. Pattern Recognition and Machine Learning. Springer 2006.
  • Ian Goodfellow, Yoshua Bengio and Aaron Courville. Deep Learning. MIT Press 2016.
  • Kevin P. Murphy. Machine Learning: A Probabilistic Perspective. MIT Press 2012.

Related research



Students are formally asked for feedback at the end of the course. Students can also submit feedback at any point here. Feedback received here will go to the Head of Academic Administration, and will be dealt with confidentially when being passed on further. All feedback is welcome.

Taking our courses

This form is not to be used by students studying for a degree in the Department of Computer Science, or for Visiting Students who are registered for Computer Science courses

Other matriculated University of Oxford students who are interested in taking this, or other, courses in the Department of Computer Science, must complete this online form by 17.00 on Friday of 0th week of term in which the course is taught. Late requests, and requests sent by email, will not be considered. All requests must be approved by the relevant Computer Science departmental committee and can only be submitted using this form.