Machine Learning Applications: Regression and Classification

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Related lectures (49)

Image Classification: Decision Trees & Random Forests Log in to Mediaspace to watch this video

Explores image classification using decision trees and random forests to reduce variance and improve model robustness.

Linear and Weighted Regression: Solution to Exercises Log in to Mediaspace to watch this video

Covers exercises on linear and weighted regression solutions, SVR problems, and Regular Least Squares.

Regression Methods: Model Building and Inference Log in to Mediaspace to watch this video

Covers Inference, Model Building, Variable Selection, Robustness, Regularised Regression, Mixed Models, and Regression Methods.

Model Selection and Evaluation: Bias-Variance Dilemma, Ridge Estimation Log in to Mediaspace to watch this video

Explores over-learning, generalization, and under-learning in machine learning models.

Nonlinear Regression: Solutions to Exercises Log in to Mediaspace to watch this video

Explores nonlinear regression using Gaussian components for density modeling.

Linear Regression Basics Log in to Mediaspace to watch this video

Covers the basics of linear regression, instrumental variables, heteroskedasticity, autocorrelation, and Maximum Likelihood Estimation.

Decision Trees: Classification Log in to Mediaspace to watch this video

Introduces decision trees for classification, covering entropy, split quality, Gini index, advantages, disadvantages, and the random forest classifier.

Regression Again: Exercise 3.1 Log in to Mediaspace to watch this video

Covers exercises on regression, including linear and polynomial regression, high dimensions, and real data analysis.

Automating Scanning Probe Microscopy: Techniques and Applications Log in to Mediaspace to watch this video

Covers the integration of machine learning with scanning probe microscopy for enhanced automation and efficiency in scientific workflows.

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