Lectures related to Pattern recognition

Covers class hierarchies, pattern matching, function values, and function calls in Scala.

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

Problem Solving Strategies: Math Word Problems

Discusses problem-solving strategies for math word problems and engaging activity design.

Artificial Intelligence Fundamentals

Covers artificial intelligence fundamentals, emphasizing practical applications and programming exercises.

Annual Meeting Awards

Announces the winners of annual meeting awards, recognizing outstanding poster and video works, and pitches from the Next Tech program.

Automatic Understanding of the Visual World

Explores machine visual perception, weakly-supervised learning, and future research in intelligent systems.

Thermal Comfort Monitoring: Non-Intrusive Solutions

Presents a framework for non-intrusive thermal comfort monitoring using computer vision and machine learning techniques.

Entity & Information Extraction

Explores information extraction using classifiers, features, and syntactic analysis.

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

Explains the conditions for using machine learning in complex problems with patterns in data and access to a dataset.

Elastic Membrane Boundary Problem 2D

Covers the elastic membrane boundary problem in 2D, exploring mathematical formulation and solution methods.

Support Vector Machines: Non-Linear Data Mapping

Explores mapping non-linear data to higher dimensions using SVM and covers polynomial feature expansion, regularization, noise implications, and curve-fitting methods.

Multilinear Regression: Design Optimization

Explores multilinear regression, design optimization, ANOVA, model fit, and experimental design.

Magistrale Awards Ceremony

Showcases the 2010 EPFL Magistrale awards ceremony with recognitions in academic, sports, and research.

Principal Component Analysis: Dimensionality Reduction

Covers Principal Component Analysis for dimensionality reduction, exploring its applications, limitations, and importance of choosing the right components.

NFNets: Removing BatchNorm for High-Performance Image Recognition

Explores NFNets as an alternative to BatchNorm in ResNets, achieving high performance on ImageNet.

Unsupervised Learning: Clustering

Explores unsupervised learning through clustering techniques, algorithms, applications, and challenges in various fields.

Image Systems

Delves into image systems, physical connections, pattern propagation, and morphograph creation.

Class Hierarchies: Pattern Matching Log in to Mediaspace to watch this video

Explores class hierarchies, case classes, enums, pattern matching, and function values in Scala.

Image Processing II: Bayesian Classification and Decision Making Log in to Mediaspace to watch this video

Explores Bayesian classification, decision making, and pattern recognition applications in image processing.

Unsupervised Learning: Dimensionality Reduction and Clustering Log in to Mediaspace to watch this video

Covers unsupervised learning, focusing on dimensionality reduction and clustering, explaining how it helps find patterns in data without labels.