'Learning algorithm' presentation slideshows

1.13k views • 88 slides

Fast Convergence of Regularized Learning in Games

Fast Convergence of Regularized Learning in Games. Vasilis Syrgkanis Microsoft Research NYC. Alekh Agarwal Microsoft Research NYC. Haipeng Luo Princeton University. Robert Schapire Microsoft Research NYC. Strategic Interactions in Computer Systems.

809 views • 66 slides

Sum-Product Networks: A New Deep Architecture

Sum-Product Networks: A New Deep Architecture. Hoifung Poon Microsoft Research Joint work with Pedro Domingos. 1. Graphical Models: Challenges. Restricted Boltzmann Machine (RBM). Bayesian Network. Markov Network. Sprinkler. Rain. Grass Wet. Advantage: Compactly represent probability.

2.21k views • 60 slides

Scientific Communication CITS7200

710 views • 60 slides

Neural Networks

Neural Networks. NNs are a study of parallel and distributed processing systems (PDPs) the idea is that the representation is distributed across a network structure an individual node itself does not have meaning, or does not represent a concept, unlike a semantic network

706 views • 56 slides

Ensemble Learning

Ensemble Learning. CS 6243 Machine Learning Modified from the slides by Dr. Raymond J. Mooney http://www.cs.utexas.edu/~mooney/cs391L And by Dr. Carla P. Gomes http://www.cs.cornell.edu/Courses/cs4700/2008fa/. Ensemble Learning.

1.58k views • 47 slides

Classification II (continued) Model Evaluation

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Towards Efficient Learning of Neural Network Ensembles from Arbitrarily Large Datasets

Towards Efficient Learning of Neural Network Ensembles from Arbitrarily Large Datasets. Kang Peng, Zoran Obradovic and Slobodan Vucetic. Center for Information Science and Technology, Temple University 303 Wachman Hall, 1805 N Broad St, Philadelphia, PA 19122, USA . Agenda. Introduction

508 views • 34 slides

General-sum games

General-sum games. You could still play a minimax strategy in general-sum games I.e., pretend that the opponent is only trying to hurt you But this is not rational:. If Column was trying to hurt Row, Column would play Left, so Row should play Down

502 views • 31 slides

An introduction to self-taught learning

An introduction to self-taught learning. Presented by: Zenglin Xu 10-09-2007. [Raina et. al, 2007] Self-taught Learning: Transfer Learning from Unlabeled Data. Outline. Related learning paradigms A self-taught learning algorithm. Related learning paradigms. Semi-supervised learning

843 views • 30 slides

Adaptive Intelligent Mobile Robots

Adaptive Intelligent Mobile Robots. Leslie Pack Kaelbling Artificial Intelligence Laboratory MIT. Two projects. Making reinforcement learning work on real robots Solving huge problems dynamic problem reformulation explicit uncertainty management. Reinforcement learning.

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Summary of the Bayes Net Formalism

Summary of the Bayes Net Formalism. David Danks Institute for Human & Machine Cognition. Bayesian Networks. Two components: Directed Acyclic Graph (DAG) G : There is a node for every variable D : Some nodes have arrows btw. them ( X ? Y )

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Introduction to Neural Networks

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CS 391L: Machine Learning: Ensembles

CS 391L: Machine Learning: Ensembles. Raymond J. Mooney University of Texas at Austin. ? ? ? ? ? ? ? ? . Training Data. Data1. Data m. Data2. ? ? ? ? ? ? ? ? . Learner m. Learner2. Learner1. ? ? ? ? ? ? ? ? . Model1. Model2. Model m. Final Model. Model Combiner.

472 views • 18 slides

CS 391L: Machine Learning: Ensembles

CS 391L: Machine Learning: Ensembles. Raymond J. Mooney University of Texas at Austin. ? ? ? ? ? ? ? ? . Training Data. Data1. Data m. Data2. ? ? ? ? ? ? ? ? . Learner m. Learner2. Learner1. ? ? ? ? ? ? ? ? . Model1. Model2. Model m. Final Model. Model Combiner.

267 views • 18 slides

Embodied Learning of Qualitative Models

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Hand Detection with a Cascade of Boosted Classifiers Using Haar-like Features

Hand Detection with a Cascade of Boosted Classifiers Using Haar-like Features. Qing Chen Discover Lab, SITE, University of Ottawa May 2, 2006. Outline. 1. Introduction 2. Haar-like features 3. Adaboost 4. The Cascade of Classifiers 5. Preliminary Results 6. Future Work. 1. Introduction.

685 views • 15 slides

LECTURE 19: FOUNDATIONS OF MACHINE LEARNING

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Self-Learning Rock Paper Scissors Robot

Self-Learning Rock Paper Scissors Robot. Bryant Pong. Q-Learning Algorithm. Four variables needed (all variables are[0, 1)): Alpha (learning rate): higher alpha is to 1, robot learns faster, but incorrect moves will affect the robot more

267 views • 11 slides