文件名称:Tutorial_Slides_by_Andrew_Moore

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  • 人工智能/神经网络/遗传算法
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  • 2012-11-26
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卡内基梅隆大学计算机系Andrew Moore教授有关人工智能算法的讲义和演讲稿,主要是模式识别方面的内容。-Andrew Moore Carnegie Mellon University computer science professor lecture about artificial intelligence algorithms and speeches, mainly pattern recognition aspects.
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下载文件列表

文件名大小更新时间
A Short Intro to Naive Bayesian Classifiers.pdf
AI Class introduction.pdf
A-star Heuristic Search.pdf
Bayesian Networks.pdf
Biosurveillance_An example.pdf
Constraint Satisfaction Algorithms with applications in Computer Vision and Scheduling.pdf
Cross-Validation.pdf
Decision Trees.pdf
Eight Regression Algorithms.pdf
Elementary probability and Naive Bayes classifiers.pdf
Game Tree Search Algorithms including Alpha-Beta Search.pdf
Gaussian Bayes Classifiers.pdf
Gaussian Mixture Models.pdf
Gaussians.pdf
Hidden Markov Models.pdf
HillClimbing Simulated Annealing and Genetic Algorithms.pdf
Inference in Bayesian Networks.pdf
Information Gain.pdf
Instance-based learning (aka Case-based or Memory-based or non-parametric).pdf
Introductory overview of time-series-based anomaly detection algorithms.pdf
K-means and Hierarchical Clustering.pdf
Learning Bayesian Networks.pdf
Machine Learning 10701 and 15781 2003 Assignment 4.doc
Markov Decision Processes.pdf
Maximum Likelihood Estimation.pdf
Neural Networks.pdf
Non-zero-sum Game Theory.pdf
PAC Learning.pdf
Predicting Real-valued Outputs_An introduction to regression.pdf
Probability Density Functions.pdf
Probability for Data Miners.pdf
Reinforcement Learning.pdf
Robot Motion Planning.pdf
Search Algorithms.pdf
Short Overview of Bayes Nets.pdf
Spatial Surveillance.pdf
Support Vector Machines.pdf
Time Series Methods.pdf
VC dimension.pdf
Zero-Sum Game Theory.pdf

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