文件名称:Tutorial_Slides_by_Andrew_Moore
- 所属分类:
- 人工智能/神经网络/遗传算法
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- [PDF]
- 上传时间:
- 2012-11-26
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- 13.38mb
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- 方*
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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 |