A Comparison of Algorithms for Inference and Learning in Probabilistic Graphical Models Brendan J. Frey and Nebojsa Jojic Abstract Computer vision is currently one of the most exciting areas of artificial intelligence re

Source URL: www.isit2004.org

• Statistics
• Statistical models
• Probability
• Graphical models
• Machine learning
• Statistical theory
• Bayesian statistics
• Markov random field
• Bayesian network
• Generative model
• Factor graph
• Discriminative model

Searching for Novel Classifiers Enrique Naredo, Leonardo Trujillo∗ , and Yuliana Martínez Doctorado en Ciencias de la Ingeniería, Departamento de Ingeniería Eléctrica y Electrónica, Instituto Tecnológico de Tijua

Source URL: eplex.cs.ucf.edu

• Genetic algorithms
• Machine learning
• Artificial intelligence
• Evolutionary algorithms
• Applied mathematics
• Evolution
• Learning
• Mathematical optimization
• Genetic programming
• Evolutionary computation
• Statistical classification
• Fitness function

Machine Learning Summer School Lecture 2: Inference and Propagation Algorithms Zoubin Ghahramani

Source URL: mlg.eng.cam.ac.uk

Very Fast Similarity Queries on Semi-Structured Data from the Web Bhavana Dalvi School of Computer Science Carnegie Mellon University

Source URL: www.cs.cmu.edu

• Statistical classification
• Statistics
• Search algorithms
• Support vector machine
• K-nearest neighbors algorithm
• K-means clustering
• Data
• Topological graph theory
• Learning
• Machine learning

Algorithms for Reinforcement Learning Draft of the lecture published in the Synthesis Lectures on Artificial Intelligence and Machine Learning series by Morgan & Claypool Publishers

Source URL: www.ualberta.ca

Evol. Intel. DOIs12065Scalable Multiagent Learning through Indirect Encoding of Policy Geometry In: Evolutionary Intelligence JournalSpringer-Verlag. DOI:s12065

Source URL: eplex.cs.ucf.edu

• Evolutionary algorithms
• Artificial neural networks
• Artificial intelligence
• Machine learning
• Applied mathematics
• Evolutionary computation
• Genetic algorithms
• HyperNEAT
• Simulation
• Neuroevolution
• Compositional pattern-producing network
• Agent-based model

arXiv:1406.1833v2 [cs.NE] 10 JunUnsupervised Feature Learning through Divergent Discriminative Feature Accumulation Paul A. Szerlip, Gregory Morse, Justin K. Pugh, and Kenneth O. Stanley Department EECS (Computer

Source URL: eplex.cs.ucf.edu

• Artificial neural networks
• Artificial intelligence
• Machine learning
• Learning
• Applied mathematics
• Evolutionary algorithms
• Computational neuroscience
• Evolutionary computation
• Neuroevolution
• Deep learning
• HyperNEAT
• Feature learning

Chapter 14 in "Online Algorithms: the state of the art", Fiat and Woeginger eds., LNCS #1442, On-Line Algorithms in Machine Learning Avrim Blum Carnegie Mellon University, Pittsburgh PAEmail: .

Source URL: www.cs.cmu.edu

Random Search for Hyper-Parameter Optimization James Bergstra and Yoshua Bengio February 10, 2011 Many machine learning algorithms have hyperparameters - flags, values, and other configuration information that guides the

Source URL: snowbird.djvuzone.org

Encouraging reactivity to create robust machines Joel Lehman, Sebastian Risi, David D’Ambrosio, and Kenneth O. Stanley To Appear In: Adaptive Behavior journal. London: SAGE, Abstract

Source URL: eplex.cs.ucf.edu

• Evolutionary algorithms
• Artificial neural networks
• Evolutionary computation
• Artificial intelligence
• Genetic algorithms
• Applied mathematics
• Machine learning
• Neuroevolution
• HyperNEAT
• Compositional pattern-producing network
• Robotics
• Evolutionary robotics