Course should give insight into the principles of design of parallel algorithms and overview of some standard parallel algorithms. The course will teach basic theoretical models of parallel computing and performance and complexity metrics of parallel algorithms. The course will also provide overview on the current industrial parallel computing systems and programming languages.
This course is intended to provide an overview of modern applications of machine learning and develop practical skills in using deep neural networks for common machine learning tasks. The course provides an introduction into artificial neural network based models, as well as an introduction to existing API frameworks for training such models. Previous knowledge regarding machine learning is not expected. The practical assignments will be developed in Python programming language.
This course is focused on the practical aspects of Machine Learning. Within the course students get familiar with with the techniques of preprocessing and visualization for data analysis. Study course provide a review of the most common algorithms for supervised and unsupervised learning, as well as an introduction to Deep Learning.
