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Related lectures (64)

Introduction to Machine Learning

Covers the basics of Machine Learning, including recognizing hand-written digits, supervised classification, decision boundaries, and polynomial curve fitting.

KNN Classifier: Nearest Neighbor Approach

Explains the K-Nearest Neighbors classifier, assigning labels based on closest points and smoothing noise in labels.

Classification with GMM

Explores the use of Gaussian Mixture Models for transitioning from clustering to classification, covering binary classification, parameter estimation, and optimal Bayes classifier.

Support Vector Machines: Linear Separability

Explores linear separability in support vector machines, focusing on hyperplane separation and margin optimization.

Land Use Mapping in the Alps

Explores soil sealing impact, land use statistics, image segmentation, and random forest classification for sustainable land management.

PHYS-467 Machine learning for physicists | Friday | Fall 24

Linear Regression: Basics

Covers the basics of linear regression, binary and multi-class classification, and evaluation metrics.

Machine Learning Fundamentals

Covers the fundamental principles and methods of machine learning, including supervised and unsupervised learning techniques.

Decision Trees and Random Forests: Concepts and Applications

Discusses decision trees and random forests, focusing on their structure, optimization, and application in regression and classification tasks.

Uniform convergence and No-Free-Lunch theorem

Explores uniform convergence, loss functions, and the No-Free-Lunch theorem in machine learning.

Generalized Linear Regression: Classification

Explores Generalized Linear Regression, Classification, confusion matrices, ROC curves, and noise in data.

Linear Regression: Basics and Applications

Covers the basics of linear regression, including 1D regression, derivatives, gradients, and applications in machine learning.

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.

Nearest Neighbor Rules: Part 2

Explores the Nearest Neighbor Rules, k-NN algorithm challenges, Bayes classifier, and k-means algorithm for clustering.

Classification: Introduction

Covers clustering, semi-supervised clustering, and binary classification formalization, along with various classification techniques.

Linear Models for Classification

Explores linear models for classification, logistic regression, decision boundaries, SVM, multi-class classification, and practical applications.

Document Analysis: Topic Modeling

Explores document analysis, topic modeling, and generative models for data generation in machine learning.

Perception: Data-Driven Approaches

Explores perception in deep learning for autonomous vehicles, covering image classification, optimization methods, and the role of representation in machine learning.

Introduction to Machine Learning MOOC: IoT Systems and Industrial Applications with Design Thinking

Covers the basics of machine learning, focusing on supervised learning algorithms and real-world applications.

Linear Models for Classification

Explores linear models for classification, including parametric models, regression, and logistic regression, along with model evaluation metrics and maximum margin classifiers.