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Related lectures (32)

Classification: Decision Trees and kNN

Introduces decision trees and k-nearest neighbors for classification tasks, exploring metrics like accuracy and AUC.

Machine Learning: Features and Model Selection

Delves into the significance of features, model evolution, labeling challenges, and model selection in machine learning.

Linear Models for Classification: Part 3

Explores linear models for classification, including binary classification, logistic regression, decision boundaries, and support vector machines.

Quantifying Performance: Misclassification and F-Measure

Covers quantifying performance through true positives, false negatives, and false positives in machine learning.

Introduction to Machine Learning

Covers the basics of machine learning for physicists and chemists, focusing on image classification and dataset labeling.

Discrete choice and machine learning: two complementary methodologies MOOC: Selected Topics on Discrete Choice

Explores discrete choice and machine learning as complementary methodologies, discussing supervised learning, model advantages, pitfalls, aggregation bias, probabilistic classification, and panel data.

Generalized Linear Regression

Explores generalized linear regression, logistic regression, and multiclass classification in machine learning.

Machine Learning Basics

Introduces the basics of machine learning, covering supervised classification, decision boundaries, and polynomial curve fitting.

Classification Problems: Overview and Loss Functions

Covers classification problems and various loss functions used in machine learning.

SVM - Principle: Linear Classifiers

Covers the history and applications of SVM, as well as the construction of linear classifiers and the concept of classifier margin.

Machine Learning: Fundamentals and Applications

Introduces machine learning basics, covering data segmentation, clustering, classification, and practical applications like image classification and face similarity.

Vapnik-Chervonenkis dimension

Covers learning bounds, complexities, growth function, shattering, and VC dimension in binary classifiers.

Logistic Regression: Probability Modeling and Optimization

Explores logistic regression for binary classification, covering probability modeling, optimization methods, and regularization techniques.

Machine Learning Biases

Explores machine learning basics, adversarial challenges, biases, distributional shift, and deployment complexities.

Statistical Inference and Machine Learning

Covers statistical inference, machine learning, SVMs for spam classification, email preprocessing, and feature extraction.

Linear Models for Classification

Explores linear models for classification, logistic regression, and gradient descent in machine learning.

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.

Multi-Class Classification: Approaches and Boundaries Log in to Mediaspace to watch this video

Explains the strategies for multi-class classification and the concept of decision boundaries.

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.

Gaussian Discriminant Rule: Classification & Boundaries Log in to Mediaspace to watch this video

Explores the Gaussian Discriminant Rule for classification using Gaussian Mixture Models and discusses drawing boundaries and model complexity.

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