Data Mining 讀書會

教材網頁：Statistical Data Mining Tutorials Tutorial Slides by Andrew Moore. http://www.autonlab.org/tutorials/

日期	題目	報告者
2006/12/2 (六) 10~12電算中心3F	Decision Trees.	阿曦
	Information Gain.
	Probability for Data Miners.
	Probability Density Functions.
	Gaussians.
	Maximum Likelihood Estimation.
	Gaussian Bayes Classifiers.
2006/12/16 (六) 10~12電算中心3F	Cross-Validation.	政道
	Neural Networks.
	Instance-based learning (aka Case-based or Memory-based or non-parametric).
	Eight Regression Algorithms.
	Predicting Real-valued Outputs: An introduction to regression.
2006/12/23 (六) 10~12電算中心3F	Bayesian Networks.	宜家
	Inference in Bayesian Networks (by Scott Davies and Andrew Moore).
	Learning Bayesian Networks.
	A Short Intro to Naive Bayesian Classifiers.
	Short Overview of Bayes Nets.
2006/12/28 (六) 10~12電算中心3F	Gaussian Mixture Models.	鎬匡
	K-means and Hierarchical Clustering.
	Hidden Markov Models.
	VC dimension.
2007/1/20 (六) 10~12電算中心3F	PAC Learning.	阿曦
	Markov Decision Processes.
	Reinforcement Learning.
2007/1/27? (六) 10~12電算中心3F	Game Tree Search Algorithms, including Alpha-Beta Search.	政道
	Zero-Sum Game Theory.
	Non-zero-sum Game Theory.
2007/2/3 (六) 10~12電算中心3F	Introductory overview of time-series-based anomaly detection algorithms.	宜家
	AI Class introduction.
	Search Algorithms.
	A-star Heuristic Search.
2007/2/10 (六) 10~12電算中心3F	Constraint Satisfaction Algorithms, with applications in Computer Vision and Scheduling.	鎬匡
	Robot Motion Planning.
	HillClimbing, Simulated Annealing and Genetic Algorithms.