## 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