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An Intuitive Tutorial to Gaussian Processes Regression (arxiv.org)

This tutorial aims to provide an intuitive understanding of the Gaussian processes regression. Gaussian processes regression (GPR) models have been widely used in machine learning applications because of their representation flexibility and inherent uncertainty measures over predictions.

Introduction to Kernel Methods for Machine Learning (seis.bristol.ac.uk)

Kernel methods give a systematic and principled approach to training learning machines and the good generalization performance achieved can be readily justified using statistical learning theory or Bayesian arguments. We describe how to use kernel methods for classification, regression and novelty detection and in each case we...

[Resource] MIT OpenCourseWare: Mathematics Of Machine Learning (ocw.mit.edu)

Broadly speaking, Machine Learning refers to the automated identification of patterns in data. As such it has been a fertile ground for new statistical and algorithmic developments. The purpose of this course is to provide a mathematically rigorous introduction to these developments with emphasis on methods and their analysis.

[Resource] Tutorial: Image Recognition with CNN in Matlab (hevpdd.ca)

Introduces neural networks, the convolution operation, a few critical machine learning concepts and some state-of-the-art CNN models. Includes a hands-on Matlab tutorial (and code) demonstrating the model configuration, training process, and performance evaluation using the MNIST dataset.

[Resource] Tutorial: State of Charge Estimation with EKF and SVSF in Matlab (hevpdd.ca)

This tutorial describes the process for the state of charge (SOC) estimation of Li-Ion cells using an equivalent circuit model. It helps students create and run a SOC estimation strategy based on the 3rd-order R-RC model in MATLAB-Simulink. The tutorial starts with a general overview of state estimation using the extended Kalman...

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