Home

Topics in machine learning

Applied sciences
Information engineering
Machine learning
Topics in machine learning

Related lectures (29)

About
Privacy
Disclaimer

Graph Chatbot

Page 2 of 3

Introduces the basics of machine learning, covering supervised classification, logistic regression, and maximizing the margin.

Linear Models for Classification

Covers linear models for classification, including SVM, decision boundaries, support vectors, and Lagrange duality.

Advanced Machine Learning: Brief review of C-SVM

Covers clustering, classification, and Support Vector Machine principles, applications, and optimization, including non-linear classification and Gaussian kernel effects.

Support Vector Machines: SVMs

Explores Support Vector Machines, covering hard-margin, soft-margin, hinge loss, risks comparison, and the quadratic hinge loss.

Multiclass Classification

Covers the concept of multiclass classification and the challenges of linearly separating data with multiple classes.

Perception: Image Classification Challenges

Covers image classification challenges, machine learning concepts, linear regression, and nearest neighbor approach in autonomous vehicles.

Introduction to Machine Learning: Supervised Learning

Introduces supervised learning, covering classification, regression, model optimization, overfitting, and kernel methods.

Support Vector Machines: Basics and Applications

Covers the basics of support vector machines, logistic regression, decision boundaries, and the k-Nearest Neighbors algorithm.

Linear Models & k-NN

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

Linear Models: Classification Basics

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