Concept
Hyperparameter
Related lectures (31)
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SVM Hyperparameters
Delves into SVM hyperparameters, showcasing the impact of C and kernel width on classification results.
Bayesian Inference: Precision in Gaussian Model
Explores Bayesian inference for precision in the Gaussian model with known mean, using a Gamma prior and discussing subjective vs objective priors.
Machine Learning Basics
Introduces machine learning basics, including data collection, model evaluation, and feature normalization.
Support Vector Regression: Nu-SVR and RVR
Explores advanced topics in machine learning, focusing on SVR extensions and hyperparameter optimization, including Nu-SVR and RVR.
Using cross-validation: Building a final predictor
Covers the interpretation of cross-validation risk estimates and building a final predictor from cross-validation results.
Simple validation, cross-validation, leave-one-out
Introduces techniques for obtaining unbiased risk estimates of learned predictors and their application for hyperparameter tuning.
Unsupervised Machine Learning: Clustering Basics
Introduces unsupervised machine learning clustering techniques like K-means, Gaussian Mixture Models, and DBSCAN, explaining their algorithms and applications.
Deep Learning for Autonomous Vehicles: Learning
Explores learning in deep learning for autonomous vehicles, covering predictive models, RNN, ImageNet, and transfer learning.