Mediaspace scheduled maintenance: Aug 25, 2026 07:00 - 12:00 AM. During this time, videos will be temporarily unavailable. Check status updates.

Home

Lecture

Machine Learning Fundamentals: Overfitting and Regularization

About
Privacy
Disclaimer

Graph Chatbot

Related lectures (38)

Regression Trees and Ensemble Methods in Machine Learning Log in to Mediaspace to watch this video

Discusses regression trees, ensemble methods, and their applications in predicting used car prices and stock returns.

Recommender Systems: Overview and Methods Log in to Mediaspace to watch this video

Explores the evolution of recommenders, collaborative filtering, Netflix Prize, model training, and optimization techniques.

Model Evaluation Log in to Mediaspace to watch this video

Explores underfitting, overfitting, hyperparameters, bias-variance trade-off, and model evaluation in machine learning.

Data Representations and Processing Log in to Mediaspace to watch this video

Discusses overfitting, model selection, cross-validation, regularization, data representations, and handling imbalanced data in machine learning.

Untitled Log in to Mediaspace to watch this video

Document Analysis: Topic Modeling Log in to Mediaspace to watch this video

Explores document analysis, topic modeling, and generative models for data generation in machine learning.

Image Classification: Overfitting and Accuracy Measures Log in to Mediaspace to watch this video

Explores overfitting and accuracy measures in image classification, emphasizing the importance of model generalization and optimal accuracy.

Statistical Learning: Fundamentals Log in to Mediaspace to watch this video

Introduces the fundamentals of statistical learning, covering supervised learning, decision theory, risk minimization, and overfitting.

Bias-Variance Trade-Off Log in to Mediaspace to watch this video

Explores underfitting, overfitting, and the bias-variance trade-off in machine learning models.

Deep Learning: Designing Neural Network Models Log in to Mediaspace to watch this video

Covers the design and optimization of neural network models in deep learning.

Data Representations & Processing Log in to Mediaspace to watch this video

Explores data representations, overfitting, model selection, cross-validation, and imbalanced data challenges.

Supervised Learning in Financial Econometrics Log in to Mediaspace to watch this video

Explores supervised learning in financial econometrics, covering linear regression, model fitting, potential problems, basis functions, subset selection, cross-validation, regularization, and random forests.

Gradient Descent Log in to Mediaspace to watch this video

Covers the concept of gradient descent, a universal algorithm used to find the minimum of a function.

Learning the Kernel: Convex Optimization Log in to Mediaspace to watch this video

Explores learning the kernel function in convex optimization, focusing on predicting outputs using a linear classifier and selecting optimal kernel functions through cross-validation.

Supervised Learning Essentials Log in to Mediaspace to watch this video

Introduces the basics of supervised learning, focusing on logistic regression, linear classification, and likelihood maximization.

State Space Models: Expressivity and Transformers Log in to Mediaspace to watch this video

Covers state space models and their expressivity compared to transformers, focusing on attention mechanisms and computational efficiency.

Dimensional Analysis: Importance and Application Log in to Mediaspace to watch this video

Explores the importance of dimensional analysis in quantifying physical quantities and solving engineering problems, emphasizing observer independence and fundamental equations.

Machine Learning Programming: Introduction Log in to Mediaspace to watch this video

Introduces the Machine Learning Programming course, covering MATLAB programming prerequisites and basics in Machine Learning, along with grading scheme and course materials.

Page 2 of 2