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Introduction to ML Idea of supervised, unsupervised, semi-supervised, reinforcement learning Linear regression Idea of model complexity, generalization, bias-variance trade-off, regularization Cross validation, VC dimension Supervised classification algorithms: K nearest neighbor, LDA, Decision Tree, SVM and kernel methods, Neural Network, Naive Bayes', Gaussian判别分析，集合方法等有关概率学习模型的更多更多信息：使用MLE，MAP，GMM，EM算法估算参数无监督的学习：群集和内核密度估计，K-Means，dbscan，parzen窗口技术等。使用PCA和内核PCA降低维度强化学习的介绍深度学习和卷积网络的简介，经常性网络
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