Skip to main content

Ctrl+K

Introduction to Scientific Machine Learning (Lecture Book)

Preface

Introduction
- Lecture 1 - Introduction to Predictive Modeling
Review of Probability
Uncertainty Propagation
Principles of Bayesian Inference
- Lecture 11 - Selecting Prior Information
- Lecture 12 - Analytical Examples of Bayesian Inference
Supervised Learning
Unsupervised Learning
- Lecture 17 - Clustering and Density Estimation
- Lecture 18 - Dimensionality Reduction
State Space Models
- Lecture 19 - State Space Models - Filtering Basics
  - State Space Models - Filtering Basics
  - Object Tracking Example
- Lecture 20 - State Space Models - Kalman Filters
  - State Space Models - Kalman Filters
  - Kalman Filter for the Object Tracking Example
Gaussian Process Regression
Neural Networks
Advanced Methods for Characterizing Posteriors
- Lecture 27 - Sampling Methods
- Lecture 28 - Variational Inference
  - Variational Inference
  - Variational Inference Examples
Homework
Bibliography

.md

Lecture 6 - Random Vectors

Contents

Objectives

Lecture 6 - Random Vectors#

Objectives#

Explain the concept of random vectors.
Introduce the various versions of the multivariate Gaussian (or Normal) distribution.

previous

Practicing with Joint Probability Mass Functions

next

Random Vectors

Contents

Objectives

By Ilias Bilionis (ibilion[at]purdue.edu)

© Copyright 2022.