Literature and Code, DD2447 and DD3342, Spring 2011

DD3342 is the research level version of DD2447

Lecture 1

Lecture 2

Lecture 3

Lecture 4

Lecture 5

Lecture 7

Lecture 8

Lecture 9

2011 LECTURE NOTES
Several corrections and clarifications. New homework texts.
Update March 04: A few typos corrected

PREPARATORY READING
Wikibook on Statistics

SUPPLEMENTARY READING (mainly DD3342)

Gelman et al: Bayesian Data Analysis

Some of Ed Jaynes' lecture notes are here:
http://www.nada.kth.se/kurser/kth/2D5342/kurspaket/

Hand on Machine Learning and the Illusion of Progress
Gammerman, Vovk, On Hedging Predictions

Vovk, et al: Algorithmic learning in a random world
Salakhutdinov, Mnih: Bayesian Probabilistic Matrix Factorization
Amazon's recommendation method
Raiko, Valpola, Harva, Karhunen: Building Blocks for Variational Bayesian Learning
Valpola, Karhunen: An Unsupervised Ensemble Learning Method for Nonlinear Dynamic

The presentation of Hedged prediction technology in fall 2011 was based on Hedging Predictions in Machine Learning, 2007 by Gammerman and Vovk. This is better connected to Kolmogorov randomness than the book and the lecture notes.

RECOMMENDED CODES

Bayes Blocks Software
Particle filtering at TKK
Mathworks central file exchange
SVM-KM package
Support Vector Technology
Bayes Net Toolbox

DD2447 Matlab code directory