文件名称:Tutorial_Slides_by_Andrew_Moore
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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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