fundamentals of machine learning for predictive data analytics

Problems:Classification, clustering, Regression, Anomaly Detection, association rules,Reinforcement learning, Structurd prediction, Feature Learning, Online learning,Semi-supervised Learning, Grammar inductionSupervised Learning:Decision Trees, ensembles (Bagging, boostring, Random Forest), k-mn, Linear regression,Nati

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At present, machine learning is one of the hottest technologies in the industry.With the rapid development of computer and network, machine learning plays a more and more important role in our life and work, and it is changing our life and work. From the daily use of the camera, daily use of the search engine, online e

Data Set Classification in machine learning with supervised (supervise), datasets are often divided into two or three groups: the training set (train set) validation set (validation set) test set. The training set is used to estimate the model, the validation set is used to determine the network structure or the parameters that control the complexity of the mod

hypothesis could not being built up,Generlly the number of hypothesisThat can is built is less than a^b.Let's come back to the inequlity, we can prove it mathematically thatif M can be replaced by a polynomial, which means the number of hypothesis in a set are not infinite and then we can declar E that learning was feasible using this hypothesis set.There is a new statement this wil be proved next lecture, if the maxnum of hypothesis are less than it

neural network are in the same form.2, for the RBF network the first level input parameters are fixed: | | x-μi| |, but for neural network, the corresponding parameters need to be learned by reverse propagation.3, for the RBF network when the first level input value is very large, the corresponding node output will become very small (Gaussian model), and for the neural network does not exist this feature, the root of the specific node used by the function. 4. RBF and Kernel methodsThen look at

Algorithms have become an important part of our daily lives, and they almost appear in any area of business. Gartner, the research firm, says the phenomenon is "algorithmic commerce", where algorithmic commerce is changing the way we operate and manage companies. Now you can buy these various algorithms for each business area on the "algorithmic market". The algorithmic market provides developers with more than 800 algorithms, including sound and visual processing,