CSC2515 - Introduction to Machine Learning
Fall 2006
Manage Weekly Readings
Student Number:
Reading:
List all submitted readings
Week 1: Valiant: A Theory of the Learnable
Week 2: Fix,Hodges: Nonparametric Discrimination
Week 3: Domingos,Pazzani: On the Optimality of the Simple Bayesian Classifier
Week 4: Robert Tibshirani: Regression shrinkage and selection via the lasso
Week 5: Rumelhart, Hinton and Williams:L Learning representation by backpropagating errors
Week 6: Mike Jordan and Robert Jacobs: Hierarchical Mixtures of Experts and the EM Algorithm
Week 7: C.K.Chow and C.N. Liu: Approximating discrete probability distributions with dependence trees
Week 8: Geoff Hinton and Radford Neal: A View of the EM Algorithm, Learning in Graphical Models
Week 9: Zoubin Ghahramani and Geoff Hinton: The EM algorithm for Mixtures of Factor Analyzers
Week 10: Alan Poritz: Hidden Markov Models, A guided tour.
Week 11: Lawrence Saul and Fernando Pereira: Aggregate and mixed-order Markov models for statistical language processing
Week 12: Rob Shapire: The Strength of Weak Learnability.
Week 13: Corrina Cortes and Vladimir Vapnik: Support-Vector Networks