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Course content:
Week 1 introduction to machine learning
Week 2 linear regression and Logistic, case: e-commerce forecasts
3 weeks of ridge regression, Lasso, variable selection techniques, and identify useful dimensions from a mess of skills
4 weeks dimension reduction techniques, case: comprehensive performance index design
5 weeks linear classifier, Knn algorithm, naive bayesian classifier, text mining, case: intelligent judgment of spam messages, through text mining to users tag, comment on automatic analysis, user loss warning
6 weeks, decision tree, combination algorithm, bagging and adaboost, random forests, case:
operator user analysis7 weeks of support vector machine (SVM), why can understand SVM rare?
8 weeks of artificial neural network, the single layer perceptron and linear neural network, BP neural network, the learning algorithm based on gradient descent, image compression and bank credit evaluation
9 weeks universal approximation radial basis function (RBF) neural network, under the new ideas look at pdas and SVM, Hopfield associative memory neural network, case: character recognition, face recognition
10 weeks of probabilistic neural network and beliefs bayesian classifier
11 weeks clustering and outlier, case: recommendation system, automatic tasting wine, cheating recognition, social system group to identify

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http://blog.csdn.net/ddjj_1980/article/details/74940593