HT 2018 - Computational Statistics SB1b/SM2

Lecturers: Prof Geoff Nicholls (lectures 1-7), Prof Fran├žois Caron (lectures 8-13)
Class tutors: Prof Fran├žois Caron, Giuseppe di Benedetto, Fadhel Ayed
Teaching Assistants: Giuseppe di Benedetto (9am class), Fadhel Ayed (3pm class), Dominic Richards (2pm class)

You will find on this webpage the material for the second half of the course. The material regarding the first part is available from Geoff Nicholls' webpage.

News





Timetable



WeekLectures

Tuesday 12:00-13:00
LG.01, 24-29 Saint-Giles'
Lectures

Friday 14:00-15:00
LG.01, 24-29 Saint-Giles'
Practicals MSc

Friday 11:00-13:00
LG.02, 24-29 Saint-Giles'
Practicals Undergrad
(assessed)
Wednesday 15:00-16:30
LG.02, 24-29 Saint-Giles'
Problem classes MSc

Tuesday 16-17
LG.01 24-29 Saint-Giles'
Problem classes Undergrad

Thursday 9-10, 14-15, 15-16
LG.04 24-29 Saint-Giles'
1XX
2XX
3XXXXX
4XXX

5XXX
X
6XXX

7
XXX
8
XXX

Lectures



Practicals


Undergraduate students
There are two assessed practicals for undergraduate students in week 4 and 8. The student submission deadlines are:
MSc students
There are two non-assessed practicals for MSc students in week 3 and week 6, and one week-long assessed practical in week 1 TT. The student submission deadline for this assessed practical is Monday 10:00 TT week 2.


Problem classes


Undergraduate students
Please hand in the solutions to the problem sheets by Tuesday 12:00 before the class (except for PS4), at the department of statistics, 24-29 Saint-Giles' (write your name and the TA's name on the script), and send the R code by email, in a single well-commented R-script to Giuseppe Di Benedetto <giuseppe.dibenedetto@spc.ox.ac.uk> (Thursday 9:00 class), Dominic Richards <dominic.richards@spc.ox.ac.uk> (Thursday 14:00 class) or Fadhel Ayed <fadhel.ayed@some.ox.ac.uk> (Thursday 15:00 class). Class allocation details are on Minerva (accessible from Oxford University network).
MSc students
There is no marking of problem sheets for MSc students. Please complete the problem sheets before the class. Solutions will be available on Weblearn.

Specimen questions on Hidden Markov models and revisions



Specimen questions on Hidden Markov models



Background reading


L. Wasserman. All of Statistics. A concise course in Statistical Inference. Chapter 8. Sptringer, 2010.
L. Wasserman. All of Nonparametric Statistics, Springer, 2005.
B. Efron, R.J. Tibshirani. An Introduction to the Bootstrap. Chapman and Hall, 1993.
B. Efron. Bootstrap methods: Another Look at the Jackknife. Annals of Statistics, Vol. 7(1), pp. 1-26, 1979. [pdf]
K.P. Murphy. Machine Learning. A probabilistic perspective. Chapters 17 and 18. The MIT Press, 2012.
D. Barber. Bayesian reasoning and machine learning. Cambridge University Press, 2012.

Last update: 21 Feb. 2018