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Related lectures (57)
Overfitting in Supervised Learning: Case Studies and Techniques
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Addresses overfitting in supervised learning through polynomial regression case studies and model selection techniques.
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.
Least Squares Solutions
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Covers least squares solutions for linear systems using matrix operations and normal systems, illustrated with examples.
Linear Models: Classification Basics
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Explores linear models for classification, logistic regression, SVM, k-NN, and curse of dimensionality.
Receiver-Operator Characteristics: ROC Curves
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Explains ROC curves, Precision-Recall curve, RMSLE, and model validation.
Machine Learning Applications: Regression and Classification
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Explores machine learning applications in materials modeling, covering regression, classification, and feature selection.
Regression Again: Exercise 3.1
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Covers exercises on regression, including linear and polynomial regression, high dimensions, and real data analysis.
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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.
Boltzmann Machine
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Covers the Boltzmann Machine, a type of stochastic recurrent neural network.
Generalized Linear Models: A Brief Review
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Provides an overview of Generalized Linear Models, focusing on logistic and Poisson regression models, and their implementation in R.
Gaussian Mixture Regression: Theory and Applications
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Explores Gaussian mixture regression and overfitting with multiple Gauss functions.
Polynomial Regression and Gradient Descent
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Covers polynomial regression, gradient descent, overfitting, underfitting, regularization, and feature scaling in optimization algorithms.
Support Vector Machines: Basics and Applications
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Covers the basics of support vector machines, logistic regression, decision boundaries, and the k-Nearest Neighbors algorithm.
Model Assessment: Metrics and Selection
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Explores model assessment metrics, selection techniques, bias-variance tradeoff, and handling skewed data distributions in machine learning.
Regression Methods: Model Building and Inference
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Covers Inference, Model Building, Variable Selection, Robustness, Regularised Regression, Mixed Models, and Regression Methods.
Edge Detection: Deep Learning Insights
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Explores the evolution of edge detection techniques, from Canny to deep learning insights.
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