Machine Learning Applications: Regression and Classification

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Related lectures (49)

Linear and Weighted Regression: Optimal Parameters and Local Solutions Log in to Mediaspace to watch this video

Covers linear and weighted regression, optimal parameters, local solutions, SVR application, and regression techniques' sensitivity.

Feature Selection, Kernel Regression, Neural Networks Playground Log in to Mediaspace to watch this video

Covers feature selection, kernel regression, and neural networks through exercises.

Binary Classification by Regression: Decision Functions and Cost Functions Log in to Mediaspace to watch this video

Explores binary classification by regression, decision functions, and various cost functions.

PAC learning framework Log in to Mediaspace to watch this video

Covers the PAC learning framework, including domain, feature set, classifier, and empirical risk.

Regression Trees and Ensemble Methods in Machine Learning Log in to Mediaspace to watch this video

Discusses regression trees, ensemble methods, and their applications in predicting used car prices and stock returns.

Deep Learning: Multilayer Perceptron and Training Log in to Mediaspace to watch this video

Covers deep learning fundamentals, focusing on multilayer perceptrons and their training processes.

Regression: Exercises Log in to Mediaspace to watch this video

Covers exercises on regression functions using RLS, WLS, and LWR.

Nonlinear Machine Learning: k-Nearest Neighbors and Feature Expansion Log in to Mediaspace to watch this video

Covers the transition from linear to nonlinear models, focusing on k-NN and feature expansion techniques.

Edge Detection: Deep Learning Insights Log in to Mediaspace to watch this video

Explores the evolution of edge detection techniques, from Canny to deep learning insights.

Machine Learning: Supervised and Unsupervised Learning Techniques Log in to Mediaspace to watch this video

Covers supervised and unsupervised learning techniques in machine learning, highlighting their applications in finance and environmental analysis.

Linear Regression: Basics and Gradient Descent Log in to Mediaspace to watch this video

Covers the basics of linear regression, including feature engineering, supervised vs. unsupervised learning, and minimizing the cost function.

Receiver-Operator Characteristics: ROC Curves Log in to Mediaspace to watch this video

Explains ROC curves, Precision-Recall curve, RMSLE, and model validation.

Kernel Methods in Machine Learning: Kernel Regression and SVM Log in to Mediaspace to watch this video

Discusses kernel methods in machine learning, focusing on kernel regression and support vector machines, including their formulations and applications.

Overfitting in Supervised Learning: Case Studies and Techniques Log in to Mediaspace to watch this video

Addresses overfitting in supervised learning through polynomial regression case studies and model selection techniques.

Linear Models for Classification: Multi-Class Extensions Log in to Mediaspace to watch this video

Covers linear models for multi-class classification, focusing on logistic regression and evaluation metrics.

Gaussian Mixture Regression: Theory and Applications Log in to Mediaspace to watch this video

Explores Gaussian mixture regression and overfitting with multiple Gauss functions.

Feature Engineering: Missing Data and Standardization Log in to Mediaspace to watch this video

Covers techniques for handling missing data and standardizing features, as well as transforming input and output data.

Regression: Interactive Lecture Log in to Mediaspace to watch this video

Covers linear regression, weighted regression, locally weighted regression, support vector regression, noise handling, and eye mapping using SVR.

Understanding Data Attributes Log in to Mediaspace to watch this video

Covers the analysis of various data attributes and linear regression models.

Untitled Log in to Mediaspace to watch this video

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