MLRG - FALL 2007

Welcome to the Machine Learning Reading Group at Brown University. The meeting time this semester is Thursday, 12-1. We will meet in the CIT third floor atrium.

MLRG meetings and other news are announced via posts to the ML-READING-GROUP list. You can subscribe at listserv.brown.edu. The MLRG leader this semester is Micha Elsner.

Our topics this semester are, provisionally, spectral clustering, variational Bayes and modeling with dynamical systems. However, we are continuing the option of 'clinic' sessions in which one member presents a research problem and we discuss various proposed solutions. If you have an ongoing or just-completed project involving machine learning, please offer to lead a session!

Date	Topic	Important People
Sept. 6	Organizational Meeting Bring your schedule!	Everyone
Sept. 13	Spectral Clustering Basics On Spectral Clustering (read sec. 1,2 and look at fig. 1) A Tutorial on Spectral Clustering (skim sec. 1,2,3,4) and consider Spike's problem from the email! (python) Slides on Graph Diffusion (expanded, with Matlab code and more visuals: PPT) and K-Means	Spike, David Jackson
Sept. 20	Spectral Clustering (Slightly Less) Basics On Spectral Clustering (continue to read) Graph Partitioning Notes Read till you get confused!	Spike, David Jackson
Sept. 27	Learning the Number of Clusters (and More...) Spectral Methods for Automatic Multiscale Clustering	Eric Sodomka, Ian Sherman
Oct. 3	Learning the Number of Clusters (Comparison with Parametrics) Continue with Spectral Methods for Automatic Multiscale Clustering Also Learning the K in K-means the Anderson-Darling test Wikipedia	No official leader
Oct. 11	Semisupervised Spectral Learning Read Spectral Learning Supplementary: Semisupervised Learning using Gaussian Fields and Harmonic Functions	Dan Grollman
Oct. 18	No-regret Learning in Convex Games No reading required! For background on no-regret, you could check out No-Phi Regret: A Connection Between Computational Learning Theory and Game Theory or other readings and slides from Amy's course last spring	Casey Marks
Oct. 25	Adaptive Distance for K-NN Improving nearest neighbor rule with a simple adaptive distance measure	David Jackson
Nov. 1	Dynamical Models: Intro Traffic Flow in 1D Cellular Automaton Model Including Cars Moving with High Speed Watch the pretty cars: simulations using the Intelligent Driver Model Slides (PDF)	Micha Elsner, Jason Pacheco
Nov. 8	Dynamical Models: Language Change The Logical Problem of Language Change	Micha Elsner (other volunteers welcome)
Nov. 15	Dynamical Models: Navigation Behavioral Dynamics of Steering, Obstacle Avoidance and Route Selection	William Warren
Nov. 29	Kalman Filtering Maybeck: Stochastic Models, Estimation and Control, chap. 1 (read first) Parameter Estimation for Linear Dynamical Systems (if you have time)	Micha Elsner (I guess)
Dec. 6	More Kalman Filtering matlab code (look at optionally)	Jason Pacheco