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CS 294  Special Topics  Berkeley Study Resources
 Berkeley
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 Hidden Markov Models: Estimation and Control (Stochastic Modelling and Applied Probability), Discrete Iterated Function Systems, Inference for Change Point and Post Change Means After a CUSUM Test (Lecture Notes in Statistics), New Kids on the Net: Activities for K12, Flexible and Distance Learning

FA05 Cs2945 Assignment 5
School: Berkeley
cs2945: Statistical Natural Language Processing Assignment 5: A Treebank Parser Due: Nov 18th Setup: The code for assignment 5 is in /home/ff/cs2945/src, or /work/cs2945/src, as usual. The data is in /home/ff/cs2945/corpora/assignment5, or /work/

Lec7
School: Berkeley
CS 2942 Grouping and Recognition Lecture 7 (Gibbs Distribution, MRF, MCMC) 9/13/99 Scribes Notes by Vito Dai Gibbs Distribution 1 ( ) = e z 1. P(X = ) > 0  U ( ) T Proving Gibbs Distribution Implies Markov Random Field Trivial because of ex

Hw4_001
School: Berkeley
CS 29434 Homework 4 Due: Thursday, November 12, 2009 Part 1: Collaborative Filtering Preliminaries The collaborative ltering section of the assignment will make use of the recently released machine learning benchmarking site, MLcomp. Before tackling the

Hw3_001
School: Berkeley
CS 294  Homework 3  Homework 3 CS 294 CS 294  Homework 2 October 15, 2009 October 2, 2006 October 15, 2009 If you have questions, contact Alexandre Bouchard (bouchard@cs.berkeley.edu) (bouchard@cs.berkeley.edu) for part 1 If you have questions, contact

Hw2_001
School: Berkeley
CS29434 Homework 2 The homework is due on October 8. Please submit a PDF on bSpace. If you have questions on the clustering questions, email Sriram; for the dimensionality reduction ones, email Percy. This assignment will be done entirely in R. You will

WaiJor08_FTML
School: Berkeley
Foundations and Trends R in Machine Learning Vol. 1, Nos. 12 (2008) 1305 c 2008 M. J. Wainwright and M. I. Jordan DOI: 10.1561/2200000001 Graphical Models, Exponential Families, and Variational Inference Martin J. Wainwright1 and Michael I. Jordan2 1 2 De