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

Home

Lecture

Linear Classification: Logistic Regression

About
Privacy
Disclaimer

Graph Chatbot

Related lectures (51)

Cross-validation & Regularization Log in to Mediaspace to watch this video

Explores polynomial curve fitting, kernel functions, and regularization techniques, emphasizing the importance of model complexity and overfitting.

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.

Linear Models for Classification: Multi-Class Extensions Log in to Mediaspace to watch this video

Covers linear models for multi-class classification, focusing on logistic regression and evaluation metrics.

Machine Learning Fundamentals Log in to Mediaspace to watch this video

Introduces fundamental machine learning concepts, covering regression, classification, dimensionality reduction, and deep generative models.

Cross-Validation: Techniques and Applications Log in to Mediaspace to watch this video

Explores cross-validation, overfitting, regularization, and regression techniques in machine learning.

Neural Networks Recap: Activation Functions Log in to Mediaspace to watch this video

Covers the basics of neural networks, activation functions, training, image processing, CNNs, regularization, and dimensionality reduction methods.

Nonlinear Machine Learning: k-Nearest Neighbors and Feature Expansion Log in to Mediaspace to watch this video

Covers the transition from linear to nonlinear models, focusing on k-NN and feature expansion techniques.

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.

Linear Models & k-NN Log in to Mediaspace to watch this video

Covers linear models, logistic regression, decision boundaries, k-NN, and practical applications in authorship attribution and image data analysis.

Linear Models: Classification Basics Log in to Mediaspace to watch this video

Explores linear models for classification, logistic regression, SVM, k-NN, and curse of dimensionality.

Decision Trees: Classification Log in to Mediaspace to watch this video

Explores decision trees for classification, entropy, information gain, one-hot encoding, hyperparameter optimization, and random forests.

Machine Learning Review Log in to Mediaspace to watch this video

Covers a review of machine learning concepts, including supervised learning, classification vs regression, linear models, kernel functions, support vector machines, dimensionality reduction, deep generative models, and cross-validation.

Dimensionality Reduction: PCA & t-SNE Log in to Mediaspace to watch this video

Explores PCA and t-SNE for reducing dimensions and visualizing high-dimensional data effectively.

Linear Models: Continued Log in to Mediaspace to watch this video

Explores linear models, logistic regression, gradient descent, and multi-class logistic regression with practical applications and examples.

Data Representation: BoW and Imbalanced Data Log in to Mediaspace to watch this video

Covers overfitting, model selection, validation, cross-validation, regularization, kernel regression, and data representation challenges.

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.

Clustering Methods Log in to Mediaspace to watch this video

Covers K-means, hierarchical, and DBSCAN clustering methods with practical examples.

Principal Component Analysis: Olympic Medals & Image Compression Log in to Mediaspace to watch this video

Explores PCA for predicting medals distribution and compressing face images.

Generalization in Learning with Random Features Log in to Mediaspace to watch this video

Explores generalization in machine learning, focusing on underfitting and overfitting trade-offs, teacher-student frameworks, and the impact of random features on model performance.

Distributed Computing: Challenges and Solutions Log in to Mediaspace to watch this video

Explores challenges in distributed computing, data growth, and data types, emphasizing the battle against the three Vs in big data.

Page 2 of 3