Linear Models for Classification: Logistic Regression and SVM

About
Privacy
Disclaimer

Graph Chatbot

Related lectures (54)

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

Introduces linear and logistic regression, covering parametric models, multi-output prediction, non-linearity, gradient descent, and classification applications.

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.

Logistic Regression: Fundamentals and Applications Log in to Mediaspace to watch this video

Explores logistic regression fundamentals, including cost functions, regularization, and classification boundaries, with practical examples using scikit-learn.

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

Explores linear models, regression, multi-output prediction, classification, non-linearity, and gradient-based optimization.

Supervised Learning Fundamentals Log in to Mediaspace to watch this video

Introduces the fundamentals of supervised learning, including loss functions and probability distributions.

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.

Classification Algorithms: Generative and Discriminative Approaches Log in to Mediaspace to watch this video

Explores generative and discriminative classification algorithms, emphasizing their applications and differences in machine learning tasks.

Logistic Regression: Probabilistic Interpretation Log in to Mediaspace to watch this video

Covers logistic regression's probabilistic interpretation, multinomial regression, KNN, hyperparameters, and curse of dimensionality.

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.

Deep Learning Fundamentals Log in to Mediaspace to watch this video

Introduces deep learning, from logistic regression to neural networks, emphasizing the need for handling non-linearly separable data.

Gradient Descent: MNIST Dataset and Logistic Loss Log in to Mediaspace to watch this video

Focuses on implementing gradient descent with the MNIST dataset and logistic loss in machine learning.

Regression: Simple and Multiple Linear Log in to Mediaspace to watch this video

Covers simple and multiple linear regression, including least squares estimation and model diagnostics.

Regularization Techniques Log in to Mediaspace to watch this video

Explores regularization in linear models, including Ridge Regression and the Lasso, analytical solutions, and polynomial ridge regression.

Statistical Measures: Mean, Median, and Dispersion Techniques Log in to Mediaspace to watch this video

Discusses statistical measures of central tendency and dispersion, focusing on mean, median, and their implications in data analysis.

Dynamic Programming in Finance Log in to Mediaspace to watch this video

Explores dynamic programming in finance, emphasizing optimal prediction strategies in market scenarios.

Convergence in Probability Log in to Mediaspace to watch this video

Explores convergence in probability, concentration inequalities, laws of large numbers, and properties of distributions.

Calibration Curves in Quantitative Ligand Binding Assays Log in to Mediaspace to watch this video

Covers the importance of calibration curves in ensuring accurate sample quantitation in ligand binding assays.

Mixing Space I Log in to Mediaspace to watch this video

Explores mixing space, ternary diagrams, greenhouse optimization, and solution scenarios.

Maximum Likelihood Estimation: Econometrics Log in to Mediaspace to watch this video

Introduces Maximum Likelihood Estimation in econometrics, covering principles, properties, applications, and specification tests.

Interval Estimation Log in to Mediaspace to watch this video

Covers the construction of confidence intervals for a normal distribution with unknown mean and variance.

Page 2 of 3