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Statistical Learning for Engineering Part 2 · LearnSpace

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Statistical Learning for Engineering Part 2

Курс от Northeastern University

Средний≈ 24.8 чАнглийский

О курсе Навыки Программа Преподаватели

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

Classification Algorithms Decision Tree Learning Statistical Machine Learning Model Optimization Predictive Modeling Machine Learning Methods Artificial Neural Networks Deep Learning Reinforcement Learning Unsupervised Learning Machine Learning Software Autoencoders Supervised Learning Machine Learning Dimensionality Reduction Machine Learning Algorithms PyTorch (Machine Learning Library)Applied Machine Learning Convolutional Neural Networks

Программа курса

7 модулей · 46 учебных материалов

01More on Kernalization and Decision Trees13 материалов

Getting Started

Course IntroductionЧтение Course OverviewВидео Meet your Course CreatorВидео Syllabus - Statistical Learning for Engineering Part 2Чтение

Учитесь у экспертов

Qurat-ul-Ain Azim

Преподаватель курса

Sivarit Sultornsanee

Преподаватель курса

В каталоге вашей программы

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Начать на Coursera

Обучение откроется на Coursera
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Обучение на Coursera

≈ 24.8 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: Kernels and Feature Maps

Kernels and Feature MapsЧтение

Lesson 2: Introduction to Decision Trees

Introduction to Decision TreesЧтение Introduction to Decision TreesВидео Decision TreesЧтение Assess Your Learning: Decision TreesЗадание

Lesson 3: Introduction to Ensemble Modeling

Introduction to Ensemble ModelingЧтение Ensemble ModelsВидео Assess Your Learning: Ensemble ModelsЗадание

02Generative Classification Models7 материалов

Lesson 1: Generative Models

Generative ModelsЧтение Discriminative vs Generative ModelsВидео Assess Your Learning: Discriminative vs Generative Models Задание

Lesson 2: Gaussian Discriminant Analysis

Gaussian Discriminant AnalysisЧтение

Lesson 3: Naive Bayes Model

Naive Bayes ModelЧтение Naive Bayes ModelВидео Assess Your Learning: Naive Bayes ModelЗадание

03Introduction to Neural Networks5 материалов

Lesson 1: Neural Network Formulation of Linear and Logistic Regression Models

Working of a Neural NetworkЧтение Working of a Neural NetworkВидео Assess Your Learning: Neural Network Formulation of Linear and Logistic Regression ModelsЗадание

Lesson 2: Forward Pass on Neural Networks

Neural Networks: Forward and Backward Pass TutorialЧтение

Lesson 3: Backpropagation

BackpropagationЧтение

04More on Neural Networks5 материалов

Lesson 1: Deep Neural Networks

Deep Neural NetworksЧтение

Lesson 2: Convolutional Neural Networks

Convolutional Neural NetworkЧтение Convolutional Neural NetworkВидео

Lesson 3: Neural Networks in Practice

Neural Networks in PracticeЧтение Neural Networks in PracticeВидео

05K-Mean Clustering and Mixture Models7 материалов

Lesson 1: Expectation Maximization Algorithms

Expectation Maximization AlgorithmsЧтение

Lesson 2: Convergence of EM Algorithms

Convergence of EM AlgorithmsЧтение

Lesson 3: K-Means Clustering and Gaussian Mixture Models

K-Means ClusteringЧтение K-Means ClusteringВидео Gaussian Mixture ModelsЧтение Gaussian Mixture ModelsВидео K-Means Clustering & Gaussian Mixture Models TutorialЧтение

06Dimensionality Reduction5 материалов

Lesson 1: Principal Component Analysis

Principal Components AnalysisЧтение Principal Component AnalysisВидео

Lesson 2: PCA and Eigenvalue Decomposition

PCA and Eigenvalue DecompositionЧтение

Lesson 3: Spectral clustering

Spectral ClusteringЧтение

Lesson 4: Autoencoders

AutoencodersЧтение

07Introduction to Reinforcement Learning4 материалов

Lesson 1: Introduction to Markov Decision Processes

Introduction to Markov Decision ProcessesЧтение Assess Your Learning: Markov Decision Processes (MDP)Задание

Lesson 2: Value Iteration and Policy Iteration

Value Iteration and Policy IterationЧтение Congratulations! Чтение