| Author | Message | ||
Dreameronaroll Junior Artist Username: Dreameronaroll Post Number: 56 Registered: 08-2015 Posted From: 47.32.252.132 Rating: N/A |
Machine learning to data mining is a part. Data mining market loki ochi many years aindhi . Machine learning picking up now. Machine learning chesthe ekkado okka chota data mining okka chapter level lo lagesthadu.. The one and only Powerstar!!! | ||
Bumper Hero Username: Bumper Post Number: 11999 Registered: 07-2014 Posted From: 104.129.196.203 Rating: N/A |
chadvithey edaina best ayee jiii. nenu rendu courses complete chesii credit tisukunna but ayee bongu teladhuu. Maa blood veru , maa breed veruuu | ||
Doosukelta Junior Artist Username: Doosukelta Post Number: 937 Registered: 04-2016 Posted From: 145.116.178.83 Rating: N/A |
kali ga undi courses teeskuntunna college lo.... ee 2 courses teeskundamani decide ayya but both time tables are clashing and fall on same time so has to choose one.... here's the syllabus of both Machine learning The aim of the course is to familiarize the student with the modern concepts of machine learning at the international research level. In particular: The student understands the concepts of Bayesian inference and use it to derive a number of different machine learning methods, such as sparse regression models, multi-layered perceptrons, graphical models and Boltzmann Machines The student is familiar with learning algorithms based on the maximum likelihood principle and Bayesian posterior estimation The student is familiar with stochastic networks of interacting variables, thermodynamic concepts, and Monte Carlos sampling methods The student is familiar with a number of approximate inference methods, such as the variational mean field method, belief propagation The student is familiar with optimal control theory, stochastic optimal control theory and path integral control theory The student is capable to write computer programs to implement the above methods Data mining: explorative data analysis (histograms, boxplots, principal component analysis); • descriptive models (clustering, association analysis, probabilistic models); • classification (decision trees, naive Bayes classifiers, nearest neighor classifiers, neural networks). On the fly, we will discuss various basic principles such as distance measures, (Bayesian) probability theory, cross-validation, and bootstrapping. ............................................................ .................... so which one do you think is the best.... idi moderator cheta cheripiveyabadindi... |