SB2b/SM4: Statistical Machine Learning

Instructor: Pier Francesco Palamara

Term: Hilary Term 2018, Jan 14 - Mar 10
Lectures: Statistics LG.01, Wed 12-13, Fri 10-11
MSc Classes: HT weeks 3,5,7 and TT week 2. Monday 4-5pm in LG.01.
MSc Practicals: Week 4 (unassessed) and week 8 (group-assessed, teams of 4)
Part B Class tutors and TAs: Tutors: Pier Francesco Palamara and Kaspar Märtens, TAs: Juba Nait Saada, Brian Zhang, and Fergus Boyles.
Part B Classes: HT weeks 3,5,7 and TT week 1. Monday 9:00am-10:30am (Pier+Fergus), 11am-12.30pm (Pier+Juba), and 1.30pm-3pm (Pier+Juba) in LG.04. Tuesday at 10:15am-11.45pm (Kaspar+Fergus) in LG.05, and 1.15pm-2:45pm (Kaspar+Brian) in LG.04.

Synopsis of course

New: Revision

You can access these past exam questions via OXAM which is on WebLearn [solutions here]. Note that the syllabus of this course is different from previous editions (e.g. it was a Part C course).
Past exam questions for revision: Revision classes and consulatation schedule (for Part B students):
(To make the consultation sessions more efficient, please send your questions by email beforehand. Pier's email: <lastname>, Kaspar's email: <firstname>

Problem Sheets

Lecture slides

Slide credits: This material was mostly prepared by others who taught similar courses and kindly shared their work, including Yee Whye Teh, Sriram Sankararaman, Dino Sejdinovic, Geoff Nicholls, Seth Flaxman, Varun Kanade, Tony Jebara, Itsik Pe'er.
Slides will be made available as the course progresses and periodically updated. Please check back for updates. Please email me to report any typos or errors.

Recommended textbooks

  • Hastie, Tibshirani and Friedman, The Elements of Statistical Learning, Springer. [ebook]
  • Bishop, Pattern Recognition and Machine Learning, Springer.
  • Murphy, Machine Learning: a Probabilistic Perspective, MIT Press, 2012.

    Further reading

  • B. D. Ripley, Pattern Recognition and Neural Networks, Cambridge University Press, 1996.
  • G. James, D. Witten, T. Hastie, R. Tibshirani, An Introduction to Statistical Learning, Springer, 2013. [ebook]
  • Resources for Python

    Resources for R

    Background Review Aids