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Lecture
Regression: Exercises
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Related lectures (49)
Gradient Descent: Linear Regression
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Covers the concept of gradient descent for linear regression, explaining the iterative process of updating parameters.
Data-Driven Modeling: Regression
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Introduces data-driven modeling with a focus on regression, covering linear regression, risks of inductive reasoning, PCA, and ridge regression.
Linear Regression and Logistic Regression
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Covers linear and logistic regression for regression and classification tasks, focusing on loss functions and model training.
Feature Selection, Kernel Regression, Neural Networks Playground
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Covers feature selection, kernel regression, and neural networks through exercises.
Machine Learning Applications: Regression and Classification
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Explores machine learning applications in materials modeling, covering regression, classification, and feature selection.
Machine Learning Fundamentals: Structure Discovery, Classification, Regression
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Covers fundamental machine learning concepts including Structure Discovery, Classification, and Regression.
Regression Trees and Ensemble Methods in Machine Learning
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Discusses regression trees, ensemble methods, and their applications in predicting used car prices and stock returns.
Kernel Methods in Machine Learning: Kernel Regression and SVM
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Discusses kernel methods in machine learning, focusing on kernel regression and support vector machines, including their formulations and applications.
Binary Classification by Regression: Decision Functions and Cost Functions
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Explores binary classification by regression, decision functions, and various cost functions.
Kernel Regression: Basics and Applications
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Explores kernel regression, the curse of dimensionality, and random features in neural networks.
PAC learning framework
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Covers the PAC learning framework, including domain, feature set, classifier, and empirical risk.
Nonlinear Machine Learning: k-Nearest Neighbors and Feature Expansion
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Covers the transition from linear to nonlinear models, focusing on k-NN and feature expansion techniques.
Edge Detection: Deep Learning Insights
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Explores the evolution of edge detection techniques, from Canny to deep learning insights.
Deep Learning: Multilayer Perceptron and Training
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Covers deep learning fundamentals, focusing on multilayer perceptrons and their training processes.
Receiver-Operator Characteristics: ROC Curves
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Explains ROC curves, Precision-Recall curve, RMSLE, and model validation.
Machine Learning: Supervised and Unsupervised Learning Techniques
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Covers supervised and unsupervised learning techniques in machine learning, highlighting their applications in finance and environmental analysis.
Decision Trees: Regression and Classification
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Covers decision trees for regression and classification, explaining tree construction, feature selection, and criteria for induction.
Regression: Interactive Lecture
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Covers linear regression, weighted regression, locally weighted regression, support vector regression, noise handling, and eye mapping using SVR.
Gaussian Mixture Regression: Theory and Applications
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Explores Gaussian mixture regression and overfitting with multiple Gauss functions.
Understanding Data Attributes
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Covers the analysis of various data attributes and linear regression models.
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