Lectures related to Computer Vision: Recap

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.

Statistical Learning Theory: Conclusions on Deep Learning

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

Landscape and Generalisation in Deep Learning

Explores the challenges and insights of deep learning, focusing on loss landscape, generalization, and feature learning.

Machine Learning Fundamentals

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

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.

Socially-aware Artificial Intelligence for Last-mile Mobility

Explores socially-aware AI for last-mile mobility, focusing on understanding social etiquettes, anticipating behaviors, and forecasting crowd movements.

Deep Learning: Convolutional Neural Networks

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

Deep Learning: No Free Lunch Theorem and Inductive Bias

Covers the No Free Lunch Theorem and the role of inductive bias in deep learning and reinforcement learning.

Deep Learning: Theory and Applications

Explores the mathematics of deep learning, neural networks, and their applications in computer vision tasks, addressing challenges and the need for robustness.

Non-Conceptual Knowledge Systems: Style Transfer and Image Translation

Explores style transfer, image translation, self-supervised learning, video prediction, and image description generation using deep learning techniques.

Deep learning in the IoT context MOOC: IoT Systems and Industrial Applications with Design Thinking

Delves into deep learning for image and object classification in IoT systems, including clustering techniques and privacy concerns.

Transformers: Unifying Machine Learning Communities Log in to Mediaspace to watch this video

Covers the role of Transformers in unifying various machine learning fields.

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.

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.

PyTorch and Convolutional Networks Log in to Mediaspace to watch this video

Covers PyTorch tensor data structure and training a CNN to classify images.

Machine Learning for Feature Extraction Log in to Mediaspace to watch this video

Explores machine learning for feature extraction, 3D vision, and neural networks in mobile robotics.

Visual Intelligence: Projects and Challenges Log in to Mediaspace to watch this video

Explores projects and challenges in visual intelligence, including hands-on learning and analysis of vision methods.

Edge Detection: Deep Learning Insights Log in to Mediaspace to watch this video

Explores the evolution of edge detection techniques, from Canny to deep learning insights.

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.

Delineation: Techniques and Dynamics Log in to Mediaspace to watch this video

Explores techniques for delineating objects in images, including dynamic programming and live wire methods.