Lectures related to Other regularizations + the Lasso

Comparing L1 and L0 + Greedy algorithms

Compares L1 and L0 penalization in linear regression with orthogonal designs using greedy algorithms and empirical comparisons.

Linear Regression: Statistical Inference and Regularization

Covers the probabilistic model for linear regression and the importance of regularization techniques.

Solving Parity Games in Practice

Explores practical aspects of solving parity games, including winning strategies, algorithms, complexity, determinism, and heuristic approaches.

L1 Regularization: Sparse Solutions and Dimensionality Reduction

Delves into L1 regularization, sparse solutions, and dimensionality reduction in the context of machine learning.

LASSO Regression: Sparse Signal Induction

Explores LASSO regression for inducing sparsity in signals through gradient descent.

The Hidden Convex Optimization Landscape of Deep Neural Networks

Explores the hidden convex optimization landscape of deep neural networks, showcasing the transition from non-convex to convex models.

Optimization Techniques: Convexity in Machine Learning

Covers optimization techniques in machine learning, focusing on convexity and its implications for efficient problem-solving.

Specification Testing and Machine Learning

Explores specification testing, machine learning, overfitting, regularization, prediction tests, and variable selection.

Introduction to Proximal Operators

Introduces proximal operators and conditional gradient methods for composite convex minimization problems in data optimization.

Geometry of the Lasso

Explores the geometric explanation of why Lasso solutions are sparse and how coefficients change with the regularization parameter.

Regularization in Machine Learning

Explores Ridge and Lasso Regression for regularization in machine learning models, emphasizing hyperparameter tuning and visualization of parameter coefficients.

Proximal and Subgradient Descent: Optimization Techniques

Discusses proximal and subgradient descent methods for optimization in machine learning.

Linear Regression and Logistic Regression Log in to Mediaspace to watch this video

Covers linear and logistic regression for regression and classification tasks, focusing on loss functions and model training.

Sparse Regression Log in to Mediaspace to watch this video

Covers the concept of sparse regression and the use of Gaussian additive noise in the context of MAP estimator and regularization.

Convex Sets: MGT-418 Lecture Log in to Mediaspace to watch this video

On Convex Optimization covers course organization, mathematical optimization problems, solution concepts, and optimization methods.

Optimization Techniques: Convexity and Algorithms in Machine Learning Log in to Mediaspace to watch this video

Covers optimization techniques in machine learning, focusing on convexity, algorithms, and their applications in ensuring efficient convergence to global minima.

Polynomial Regression and Gradient Descent Log in to Mediaspace to watch this video

Covers polynomial regression, gradient descent, overfitting, underfitting, regularization, and feature scaling in optimization algorithms.

Gradient Descent: Optimization Techniques Log in to Mediaspace to watch this video

Explores gradient descent, loss functions, and optimization techniques in neural network training.

Convex Optimization: Convex Functions Log in to Mediaspace to watch this video

Covers the concept of convex functions and their applications in optimization problems.

KKT and Convex Optimization Log in to Mediaspace to watch this video

Covers the KKT conditions and convex optimization, discussing constraint qualifications and tangent cones of convex sets.