courseraИнженерия

Foundations of Statistical Learning & Algorithms

Курс от Northeastern University

Уровень не указан≈ 21.4 чАнглийский

О курсе

This course covers linear algebra, probability, and optimization. It begins with systems of equations, matrix operations, vector spaces, and eigenvalues. Advanced topics include Cholesky and singular value decomposition. Probability modules address Bayes' theorem, Gaussian distribution, and inference techniques. The course concludes with model selection methods and an introduction to optimization.

Навыки, которые вы освоите

Linear Algebra Bayesian Statistics Model Optimization Probability Dimensionality Reduction Applied Mathematics Machine Learning Algorithms Algebra Model Evaluation Probability Distribution Probability & Statistics Statistical Inference Machine Learning Machine Learning Methods Statistical Machine Learning Statistical Modeling Statistical Methods Verification And Validation

Программа курса

4 модулей · 72 учебных материалов

01Introduction to Linear Algebra and Vector Spaces29 материалов

Getting Started

Course IntroductionВидео Course OverviewЧтение SyllabusЧтение Meet Your InstructorВидео

Учитесь у экспертов

Rehab Ali

Преподаватель курса

Foundations of Statistical Learning & Algorithms

О курсе

Навыки, которые вы освоите

Программа курса

Getting Started

Учитесь у экспертов

Rehab Ali

Lesson 1: Introduction to Linear Algebra

Lesson 2: Systems of Linear Equations

Lesson 3: Matrices

Lesson 4: Vector Spaces

Lesson 5: Orthogonal Complement and Projection

Linear Independence

Linear Mapping

Lesson 3: Eigenvalues and Eigenvectors

Lesson 4: Cholesky Decomposition

Lesson 5: Singular Value Decomposition

Lesson 1: Sum Rule, Product Rule, and Bayes' Theorem

Lesson 2: Gaussian Distribution

Lesson 3: Inference Techniques

Lesson 1: Model Selection Techniques

Lesson 2: Cross Validation

Lesson 3: Introduction to Optimization Techniques