Zoubin Ghahramani Professor of Information Engineering Department of Engineering University of Cambridge Chief Scientist Uber UBER Turing Fellow Alan Turing Institute London Deputy Academic Director Leverhulme Centre for the Future of Intelligence CFI Fellow St John's College Cambridge

Machine Learning Group @ Cambridge The Automatic Statistician PhD Admissions Research Papers [New] [Older] Research Interests Former Students and Postdocs Contact Information CV and Short Bio

Students and Postdocs: Matej Balog John Bradshaw Karolina Dziugaite Hong Ge Shane Gu Dave Janz Maria Lomeli Alex Matthews Adam Scibior Amar Shah Christian Steinruecken

News and Older News

Research Areas and Papers by Topic

For recent publications see the Machine Learning Group Publications Webpage

	Gaussian Processes		Clustering		Graphical Models
	Monte Carlo Methods		Semi-Supervised Learning		Non-parametric Bayesian Methods
	Approximate Inference		Bioinformatics		Information Retrieval
	Sensorimotor Control and Robotics		Time Series Models		Review Articles and Tutorials

Talks and Tutorials:

• 2013 Machine Learning Summer School Lectures, Tuebingen on probabilistic modelling and Bayesian inference (videos and slides)
• 2012 Machine Learning Summer School Lectures, La Palma: Bayesian Modelling, Graphical Models, and Semi-supervised Learning
• Probabilistic Modelling, Machine Learning and the Information Revolution, MIT CSAIL Talk 2012.
• Bayesian Hidden Markov Models and Extensions, invited talk at CoNLL 2010, Uppsala, Sweden.
• A Brief Overview of Nonparametric Bayesian Models presented at the NIPS 2009 Workshop on Nonparametric Bayesian Methods.
• The Indian Buffet Process and Extensions presented at the 7th Workshop on Bayesian Nonparametrics, Turin 2009.
• Video Lectures of the Machine Learning Summer School
• Video Lectures available online on Graphical Models, Bayesian Learning, and Non-parametric Bayesian methods.
• UAI 2005 Tutorial on Nonparametric Bayesian Methods
• ICML 2004 Tutorial on Bayesian Machine Learning
• NIPS 1999 Tutorial on Probabilistic Models for Unsupervised Learning
• An Old Talk on Bayesian Model Comparison

Teaching:

• Machine Learning (4F13), Lent, Cambridge
• Signal and Pattern Processing (3F3), Lent, Cambridge
• Image Searching and Modelling (Part IB, Paper 8), Cambridge
• Unsupervised Learning Course (GI02), Fall 2005, UCL
• Gatsby Machine Learning Qualifying Exam Topic List
• Statistical Approaches to Learning and Discovery, Spring 2002, CMU
• Old Course Websites

Conferences and Workshops:

• International Conference on Machine Learning (ICML 2007), Portland, Oregon
• Open Issues in Gaussian Processes for Machine Learning, NIPS Workshop, Vancouver, Dec 10, 2005
• The 10th International Workshop on Artificial Intelligence and Statistics, Barbados, Jan 6-8, 2005
• Workshop on Learning Theoretic and Bayesian Inductive Principles, London, July 19-21, 2004
• NIPS Conference

Other:

• The Automatic Statistician
• Very Brief Introduction to Bayesian Machine Learning
• Software
• www.Variational-Bayes.org website
• Interested in a PhD in Machine Learning? Apply to our group at the University of Cambridge.
• CV and Short Bio
• Memorial Site for my friend and colleague, Sam Roweis, 1972-2010
• Miscellaneous

Academic Links:

• The Machine Learning @ Cambridge University website.
• My web page at the Machine Learning Department, Carnegie Mellon University
• The Machine Learning Centre at University College London
• Journal of Machine Learning Research
• Bayesian Analysis
• IEEE Transactions on Pattern Analysis and Machine Intelligence
• Machine Learning
• PASCAL Research Network
• Many other machine learning links

Contact Information:

Mail: Department of Engineering University of Cambridge Trumpington Street Cambridge CB2 1PZ, UK Office: BE-449

Tel: Office  +44 (0)1223 748 531 Fax     +44 (0)1223 332 662 Email: zoubin - at - eng.cam.ac.uk (replace -at- by @)

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