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Курс от Northeastern University 
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Machine Learning for Engineers: Algorithms and Applications
Курс от Northeastern University О курсе
This course covers practical algorithms and the theory for machine learning from a variety of perspectives. Topics include supervised learning (generative, discriminative learning, parametric, non-parametric learning, deep neural networks, support vector Machines), unsupervised learning (clustering, dimensionality reduction, kernel methods). The course will also discuss recent applications of machine learning, such as computer vision, data mining, natural language processing, speech recognition and robotics. Students will learn the implementation of selected machine learning algorithms via python and PyTorch.
Навыки, которые вы освоите
Supervised LearningModel OptimizationRegression AnalysisMachine Learning MethodsMachine Learning AlgorithmsStatistical Machine LearningModel EvaluationStatistical ModelingUnsupervised LearningDimensionality ReductionApplied Machine LearningMachine Learning SoftwareAlgorithmsMachine LearningModel TrainingArtificial Intelligence and Machine Learning (AI/ML)Complex Problem SolvingPyTorch (Machine Learning Library)Statistical AnalysisStatistical Methods
Программа курса
4 модулей · 34 учебных материалов
01Introduction to Statistical Learning in Engineering10 материалов
Getting Started: Machine Learning for Engineers-- Algorithms & Applications
Course OverviewЧтениеSyllabus - Introduction to Service Innovation and ManagementЧтениеMeet Your Fellow LearnersОбсуждениеAcademic IntegrityЧтениеLesson 1: The Idea Behind Statistical Learning
Учитесь у экспертов
Qurat-ul-Ain Azim
Преподаватель курса