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WhatsApp + 91-9999487949
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Send Email info@softapptechnologies.com
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Office No-51/3, 1st floor, Corenthum tower-B, near electronic city metro station, Sector 62, Noida, Uttar Pradesh 201309a
Machine Learining Course
This introductory course gives an overview of many concepts, techniques, and algorithms in machine learning witch means machine learning is part from airtificial intellgence , beginning with topics such as classification and linear regression and ending up with more recent topics such as boosting, support vector machines, hidden Markov models, and Bayesian networks.
Introduction, linear classification, perceptron update rule
Linear regression, estimator bias and variance, active learning Perceptron convergence, generalization
Kernal regression, kernels
Maximum margin classification
Classification errors, regularization, logistic regression
Midterm
Support vector machine (SVM) and kernels, kernel optimization
Active learning (cont.), non-linear predictions, kernals
Margin and generalization, mixture models
Model selection
oosting, margin, and complexity
Model selection criteria
Description length, feature selection
Combining classifiers, boosting
Mixtures and the expectation maximization (EM) algorithm
EM, regularization, clustering
Clustering
Hidden Markov models (HMMs)
Spectral clustering, Markov models
HMMs (cont.)
Probabilistic inference
Bayesian networks
Learning Bayesian networks
Guest lecture on collaborative filtering
Current problems in machine learning, wrap up
Linear regression, estimator bias and variance, active learning Perceptron convergence, generalization
Kernal regression, kernels
Maximum margin classification
Classification errors, regularization, logistic regression
Midterm
Support vector machine (SVM) and kernels, kernel optimization
Active learning (cont.), non-linear predictions, kernals
Margin and generalization, mixture models
Model selection
oosting, margin, and complexity
Model selection criteria
Description length, feature selection
Combining classifiers, boosting
Mixtures and the expectation maximization (EM) algorithm
EM, regularization, clustering
Clustering
Hidden Markov models (HMMs)
Spectral clustering, Markov models
HMMs (cont.)
Probabilistic inference
Bayesian networks
Learning Bayesian networks
Guest lecture on collaborative filtering
Current problems in machine learning, wrap up