Machine Learning: 2024-2025

Lecturer

Seth Flaxman

Degrees

Schedule B1 (CS&P) — Computer Science and Philosophy

Schedule A2 — Computer Science

Schedule B1 — Computer Science

Schedule A2(M&CS) — Mathematics and Computer Science

Schedule B1(M&CS) — Mathematics and Computer Science

Michaelmas Term — MSc in Advanced Computer Science

Term

Michaelmas Term 2024 (20 lectures)

Overview

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.

Prerequisites

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.

Syllabus

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.
Trevor Hastie, Robert Tibshirani, and Jerome Friedman. The Elements of Statistical Learning. Springer 2009.

Related research

Themes

Artificial Intelligence and Machine Learning

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.