Machine Learning: 20162017
Lecturer 

Degrees 
Schedule B2 (CS&P) — Computer Science and Philosophy Schedule B2 — Computer Science 
Term 
Michaelmas Term 2016 (16 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, questionanswering, 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 solve new tasks, related to previously encountered tasks, more efficiently.
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 webpages based on the topics they discuss. Students will learn the 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 realworld problems.
N.B. There is no past paper for the 201617 Machine Learning Course. Whilst you may refer to the 201516 Machine Learning
exam in preparation, please note that the content and scope of the 201617 version will be different.
Prerequisites
Machine learning is a mathematical discipline and it is helpful to have a good background in linear algebra, calculus, probability and algorithms. If you have not take the following courses (or their equivalents) you should talk to the instructor prior to registering for the class.
 Continuous Mathematics
 Linear Algebra
 Design and Analysis of Algorithms
Syllabus

Reading list
 Kevin P. Murphy. Machine Learning: A Probabilistic Perspective. MIT Press 2012
 Christopher M. Bishop. Pattern Recognition and Machine Learning. Springer 2007
 Trevor Hastie, Robert Tibshirani, Jerome Friedman. The Elements of Statistical Learning. Springer 2011