Mediaspace scheduled maintenance: Aug 25, 2026 07:00 - 12:00 AM. During this time, videos will be temporarily unavailable. Check status updates.

Concept

Automatic image annotation

Related lectures (32)

Machine Learning Fundamentals

Covers fundamental principles, opportunities, and challenges in machine learning.

Supervised Learning: k-NN and Decision Trees

Introduces supervised learning with k-NN and decision trees, covering techniques, examples, and ensemble methods.

Evaluating Machine Accuracy and Robustness on ImageNet

Explores the evaluation of machine and human accuracy and robustness on ImageNet, highlighting progress, challenges, and the need for improvement.

Image Classification: Other Data Types

Covers image classification with different data inputs and outputs.

Introduction to Machine Learning

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

Machine Learning: Fundamentals and Applications

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

Machine Learning for Solving PDEs: Random Feature Method

Explores the Random Feature Method for solving PDEs using machine learning algorithms to approximate high-dimensional functions efficiently.

Deep Learning: Convolutional Neural Networks

Covers Convolutional Neural Networks, standard architectures, training techniques, and adversarial examples in deep learning.

Artificial Intelligence: The New Radical Addition to Work

Explores the convergence of IoT, Big Data, and Deep Learning in Artificial Intelligence applications across various industries.

Convolutional Neural Networks

Introduces Convolutional Neural Networks, covering fully connected layers, convolutions, pooling, PyTorch translations, and applications like hand pose estimation and tubularity estimation.

Statistical Learning Theory: Conclusions on Deep Learning

Covers the conclusions on deep learning and an introduction to statistical learning theory.

Visual Recognition: Integrated 3D + Semantics Log in to Mediaspace to watch this video

Covers visual recognition models, scene graphs, neural networks, and embodied vision.

Understanding ImageNet Classifiers Log in to Mediaspace to watch this video

Explores the generalization of ImageNet classifiers and the challenges in achieving reliable machine learning models.

Robust Vision: Data Augmentation and Ensembling Techniques Log in to Mediaspace to watch this video

Explores data augmentation and ensembling techniques for robust vision, comparing ViT and ResNet models.

Social Image Systems: From Past to Present Log in to Mediaspace to watch this video

Explores the evolution of social image systems, deep learning models, selfies, and biometrics in online platforms.

Computer Vision: Recap Log in to Mediaspace to watch this video

Recaps key concepts in computer vision, challenges, opportunities, and upcoming exam details.

Machine Learning for Physicists/Chemists: Image Classification Log in to Mediaspace to watch this video

Covers the fundamentals of machine learning for physicists and chemists, focusing on image classification tasks using artificial intelligence.

Optical Computing: All-Analog Photoelectronic Chip for Vision Tasks Log in to Mediaspace to watch this video

Presents an all-analog photoelectronic chip for high-speed vision tasks, addressing challenges in classical computation and proposing a hybrid optical-electrical framework.

Fully Connected Networks on MNIST and SUSY Datasets Log in to Mediaspace to watch this video

Covers the implementation of fully connected neural networks on two datasets using PyTorch.

Transformers in Vision: Applications and Architectures Log in to Mediaspace to watch this video

Covers the impact of transformers in computer vision, discussing their architecture, applications, and advancements in various tasks.