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Statistical Learning for Engineering Part 1 · LearnSpace
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Statistical Learning for Engineering Part 1

Курс от Northeastern University
Средний≈ 35.6 чАнглийский
О курсеНавыкиПрограммаПреподаватели

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 LearningStatistical ModelingRegression AnalysisStatistical Machine LearningMachine Learning AlgorithmsModel OptimizationUnsupervised LearningModel TrainingStatistical MethodsPredictive ModelingStatistical SoftwareDeep LearningPyTorch (Machine Learning Library)Applied Machine LearningMachine Learning SoftwareMachine Learning MethodsPredictive AnalyticsArtificial Intelligence and Machine Learning (AI/ML)Statistical ProgrammingMachine Learning

Программа курса

7 модулей · 49 учебных материалов

01Introduction to Statistical Learning in Engineering11 материалов

Getting Started

Course IntroductionЧтениеCourse OverviewВидеоSyllabus - Statistical Learning for Engineering Part 1ЧтениеMeet your Course CreatorВидео

Учитесь у экспертов

Qurat-ul-Ain Azim

Преподаватель курса

Sivarit Sultornsanee

Преподаватель курса

Statistical Learning for Engineering Part 1
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Обучение на Coursera

≈ 35.6 ч

7 модулей

Язык: Английский

Субтитры: Арабский, Французский, Узбекский, Итальянский, Бразильский португальский, Корейский, Немецкий, Испанский, Японский, Казахский, Венгерский

Часть программы вашего университета
Academic IntegrityЧтение

Lesson 1: The Idea Behind Statistical Learning

Statistical Learning OverviewВидеоAssess Your Learning: Statistical Learning OverviewЗадание

Lesson 2: Example Applications of Statistical Learning

Supervised and Unsupervised LearningЧтение

Lesson 3: Tools and Techniques Required for the Course

Probability TutorialЧтениеCalculus TutorialЧтениеLinear Algebra TutorialЧтение
02A Primer on Statistical Learning Concepts7 материалов

Lesson 1: Maximum Likelihood Estimation

Maximum Likelihood EstimationЧтениеMaximum Likelihood EstimationВидеоAssess Your Learning: Maximum Likelihood EstimationЗадание

Lesson 2: Convex Optimization

Convex OptimizationЧтение

Lesson 3: Gradient Descent Algorithm

Gradient DescentЧтениеGradient DescentВидеоAssess Your Learning: Gradient DescentЗадание
03The Learning Process8 материалов

Lesson 1: Components of a Learning Process

Components of a Learning ProcessЧтениеComponents of a Learning ProcessВидеоAssess Your Learning: Components of a Learning ProcessЗадание

Lesson 2: Model Training and Evaluation

Model Training and EvaluationЧтение

Lesson 3: Overfitting vs. Underfitting

Overfitting vs UnderfittingЧтение

Lesson 4: Bias-Variance Trade-Off

Bias Variance Trade-OffЧтениеBias Variance Trade-OffВидеоAssess Your Learning: Bias Variance Trade-OffЗадание
04Linear Regression5 материалов

Lesson 1: Linear Regression Model Formulation

Linear Regression Model FormulationЧтениеLinear Regression OverviewВидеоAssess Your Learning: Linear Regression Model FormulationЗадание

Lesson 2: Solution Techniques for Ordinary Least

Solution Techniques for Ordinary LeastЧтение

Lesson 3: Gradient Descent

Gradient DescentЧтение
05More on Regression5 материалов

Lesson 1: Regularization

Regularization for Linear RegressionЧтениеRegularization for Linear RegressionВидеоAssess Your Learning: RegularizationЗадание

Lesson 2: Ridge and Lasso Regularizations

Ridge and Lasso RegularizationsЧтение

Lesson 3: Polynomial Regression

Polynomial RegressionЧтение
06Logistic Regression6 материалов

Lesson 1: Logistic Regression Model

Logistic Regression ModelЧтениеLogistic Regression OverviewВидеоAssess Your Learning: Logistic Regression ModelЗадание

Lesson 2: Maximum Likelihood Estimation

Maximum Likelihood EstimationЧтение

Lesson 3: Generalized Linear Classification Models

Generalized Linear Classification ModelsЧтениеGeneralized Linear Classification ModelsВидео
07Support Vector Machines and Kernelization7 материалов

Lesson 1: Margins and Kernels

Margins and KernelsЧтение

Lesson 2: Support Vector Machines

Support Vector MachinesЧтениеSupport Vector MachinesВидеоSupport Vector MachinesЧтениеAssess Your Learning: Support Vector MachinesЗадание

Lesson 3: Types of Kernels and Hyperparameter Tuning

Types of Kernels and Hyperparameter TuningЧтениеCongratulations! Чтение