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Related lectures (32)

Page 2 of 4

Advanced Machine Learning: Feature Selection

Explores machine learning algorithms, feature selection techniques like FAST and BRIEF descriptors, and the limitations of deep learning.

Multi-Criteria Analysis: Investment Choice

Covers multi-criteria decision analysis for investment choice and sustainability, including challenges in criteria selection and scoring methods.

Support Vector Machines: Interactive Class

Explores Support Vector Machines in machine learning, discussing SVM, support vectors, uniqueness of solutions, and multi-class SVM.

Linear Models for Classification: Multi-Class Extensions

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

Support Vector Machines: Parameters, Solutions, and Boundaries

Explores SVM parameters, solutions, and decision boundaries, including the uniqueness of solutions and the impact of kernel width.

Geometric Insights on Deep Learning Models

Delves into the geometric insights of deep learning models, exploring their vulnerability to perturbations and the importance of robustness and interpretability.

Market Mechanisms: Crowdfunding, Prediction Markets, Quadratic Voting

Explores crowdfunding, prediction markets, and quadratic voting as market-based mechanisms.

Linear Models: Classification Basics

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

Interpretable Machine Learning: Sparse Decision Trees and Interpretable Neural Networks

Explores the extremes of interpretability in machine learning, focusing on sparse decision trees and interpretable neural networks.

Distribution Estimation

Covers the estimation of distributions using samples and probability models.