An activity monitoring system for elderly care using generative and discriminative models
Personal and Ubiquitous Computing
Lagrange dual decomposition for finite horizon Markov decision processes
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part I
Structured Learning and Prediction in Computer Vision
Foundations and Trends® in Computer Graphics and Vision
Genetic Programming and Evolvable Machines
Mixture modeling of gait patterns from sensor data
Proceedings of the 5th International Conference on PErvasive Technologies Related to Assistive Environments
Colour matching function learning
SSPR'12/SPR'12 Proceedings of the 2012 Joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
Towards high-throughput gibbs sampling at scale: a study across storage managers
Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data
Speeding up large-scale learning with a social prior
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Variational inference in nonconjugate models
The Journal of Machine Learning Research
Dynamic analysis of naive adaptive brain-machine interfaces
Neural Computation
Dynamic analysis of naive adaptive brain-machine interfaces
Neural Computation
Evaluating simulation software components with player rating systems
Proceedings of the 6th International ICST Conference on Simulation Tools and Techniques
Deep learning of representations: looking forward
SLSP'13 Proceedings of the First international conference on Statistical Language and Speech Processing
Geographic aspects of tie strength and value of information in social networking
Proceedings of the 6th ACM SIGSPATIAL International Workshop on Location-Based Social Networks
Networked individuals predict a community wide outcome from their local information
Decision Support Systems
Gaussian Kullback-Leibler approximate inference
The Journal of Machine Learning Research
Perturbative corrections for approximate inference in Gaussian latent variable models
The Journal of Machine Learning Research
Review: A review of novelty detection
Signal Processing
An overview of bayesian methods for neural spike train analysis
Computational Intelligence and Neuroscience - Special issue on Modeling and Analysis of Neural Spike Trains
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Machine learning methods extract value from vast data sets quickly and with modest resources. They are established tools in a wide range of industrial applications, including search engines, DNA sequencing, stock market analysis, and robot locomotion, and their use is spreading rapidly. People who know the methods have their choice of rewarding jobs. This hands-on text opens these opportunities to computer science students with modest mathematical backgrounds. It is designed for final-year undergraduates and master's students with limited background in linear algebra and calculus. Comprehensive and coherent, it develops everything from basic reasoning to advanced techniques within the framework of graphical models. Students learn more than a menu of techniques, they develop analytical and problem-solving skills that equip them for the real world. Numerous examples and exercises, both computer based and theoretical, are included in every chapter. Resources for students and instructors, including a MATLAB toolbox, are available online.