Artificial Intelligence
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The document discusses different machine learning algorithms for instance-based learning. It describes k-nearest neighbor classification which classifies new instances based on the labels of the k closest training examples. It also covers locally weighted regression which approximates the target function based on nearby training data. Radial basis function networks are discussed as another approach using localized kernel functions to provide a global approximation of the target function. Case-based reasoning is presented as using rich symbolic representations of instances and reasoning over retrieved similar past cases to solve new problems.
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* Overfitting (intro)
* Curse of dimensionality
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* Linear regression
[ppt]
The document provides an overview of various machine learning classification algorithms including decision trees, lazy learners like K-nearest neighbors, decision lists, naive Bayes, artificial neural networks, and support vector machines. It also discusses evaluating and combining classifiers, as well as preprocessing techniques like feature selection and dimensionality reduction.
[ppt]
This document provides an overview of various machine learning classification techniques including decision trees, k-nearest neighbors, decision lists, naive Bayes, artificial neural networks, and support vector machines. For each technique, it discusses the basic approach, how models are trained and tested, and potential issues that may arise such as overfitting, parameter selection, and handling different data types.
Poggi analytics - distance - 1a
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ANSSummer2015
This document describes a multi-level reduced order modeling approach with robust error bounds. It discusses applying dimensionality reduction algorithms to extract active subspaces from reduced complexity models, then equipping the reduced model with an error bound. It presents a case study applying this approach to a 7x7 nuclear fuel assembly benchmark model, extracting active subspaces from individual fuel pin cell models to build a reduced order model in a more computationally efficient way.
Development of Multi-Level ROM
This document describes a multi-level reduced order modeling approach with robust error bounds. It discusses applying dimensionality reduction algorithms to extract active subspaces from reduced complexity models, then equipping the reduced model with an error bound. A case study applies this approach to a nuclear reactor assembly model by extracting active subspaces from individual pin cell models to build a reduced order model in a more computationally efficient way than using the full assembly model.
Multilayer Perceptron (DLAI D1L2 2017 UPC Deep Learning for Artificial Intell...
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https://telecombcn-dl.github.io/2017-dlai/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks or Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles of deep learning from both an algorithmic and computational perspectives.Knn 160904075605-converted
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Support Vector Machines
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