Concept
Overfitting
Related lectures (160)
Convergence of Gradient Descent
Explores the convergence of gradient descent for strongly convex functions and the importance of regularization in preventing overfitting.
Estimation Methods: Bias-Variance Tradeoff
Explores the MSE quality measure for estimators and the bias-variance tradeoff.
Financial Time Series: GARCH Processes
Covers GARCH processes for financial time series analysis.
Frequency estimation for stochastic signals (large noise)
Covers numerical experiments for frequency estimation of stochastic signals.
Fitting and Clustering Data with Mixture of Gauss Functions
Covers Mixture of Gauss Functions, Gaussian Mixture Modeling, and hyper-parameter optimization.
Multi-linear regression
Covers the concept of multi-linear regression and the least squares method for model fitting.
Quantitative Risk Management: Copulas and Generative Adversarial Networks
Explores copulas, simulation algorithms, fitting data with rank correlations, and GANs for image generation.
Regularization: Promoting Optimal Solutions
Covers regularization in least-squares problems, promoting optimal solutions while addressing challenges like non-uniqueness, ill-conditioning, and over-fitting.
Machine Learning: Brain Imaging and Classifier Principles
Explores machine learning in brain imaging, focusing on spatial patterns, emotions, and classifier trade-offs.
Understanding Generalization: Implicit Bias & Optimization
Explores the trade-off between complexity and risk in machine learning models, the benefits of overparametrization, and the implicit bias of optimization algorithms.