… blends with parallel developments in computer science and, in particular, machine learning.
The field encompasses many methods such as the lasso and sparse regression, classification and regression trees, and boosting and support vector machines. With the explosion of “Big Data” problems, statistical learning has become a very hot field in many scientific areas as well …
… structures which will be widely used in future are also included. After data structures are finalized next step is designing the algorithm to solve the problem. Hence creeps up the need to analyze the algorithm’s complexity.
One problem and be solved with multiple approaches, therefore the algorithm which has the best complexity is chosen …
… and cognitive models and how to train their models using supervised, unsupervised and various other models of learning.
It focuses on machine learning concepts like the Decision tree, SVM, Bayesian, Neural network, K-nearest neighbours, Q-learning, Genetic algorithm, Markov decision processes, Convolutional neural networks, Linear regression or logistic regression, Boosting, bagging, ensemble, Random hill …
