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

Home

Lecture

Machine Learning Applications: Predicting Carbon Nanotube Properties

About
Privacy
Disclaimer

Graph Chatbot

Related lectures (38)

Structural Mechanics Fundamentals Log in to Mediaspace to watch this video

Covers the fundamental concepts of structural mechanics, including materials properties, stress and strain, equilibrium of forces and moments, and elementary structures.

Axial Loading: Stress and Strain in Deformable Bodies Log in to Mediaspace to watch this video

Covers axial loading, stress, strain, and their implications in deformable bodies.

Towards Sustainable Composites Log in to Mediaspace to watch this video

Explores sustainable composite solutions, including recycling, end-of-life strategies, and hydrogen storage.

Cost Modelling for Sustainable Innovation Log in to Mediaspace to watch this video

Introduces cost modelling as a tool for sustainable innovation and covers topics such as transport CO2 emissions and material properties.

Document Analysis: Topic Modeling Log in to Mediaspace to watch this video

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

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.

Failure Mechanisms in MEMS Log in to Mediaspace to watch this video

Explores failure mechanisms in MEMS, covering design, manufacturing, and in-use failures, emphasizing reliability considerations and mitigation strategies for mechanical shock and vibration resistance.

Regression Trees and Ensemble Methods in Machine Learning Log in to Mediaspace to watch this video

Discusses regression trees, ensemble methods, and their applications in predicting used car prices and stock returns.

Mechanical Behavior of Materials: Deformation & Fatigue Log in to Mediaspace to watch this video

Explores deformation mechanisms, cyclic loading, and fatigue phenomena in materials.

Deep Learning Fundamentals Log in to Mediaspace to watch this video

Introduces deep learning fundamentals, covering data representations, neural networks, and convolutional neural networks.

Mechanical Joining: Fasteners and Integral Joints Log in to Mediaspace to watch this video

Explores mechanical joining techniques through fasteners and integral joints, discussing their advantages, limitations, and specific applications.

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.

Visual Intelligence: Machines and Minds Log in to Mediaspace to watch this video

Explores visual intelligence, image formation, computer vision, and representation understanding in machines and minds.

Photonic Extreme Learning Machines: Reservoir Computing Techniques Log in to Mediaspace to watch this video

Covers photonic extreme learning machines and reservoir computing, focusing on their architectures, programming techniques, and applications in optical computing.

Fracture Mechanics: Crack Growth and Stress Intensity Factor Log in to Mediaspace to watch this video

Explores crack growth, stress intensity factor, and fracture toughness in materials.

Stress and Deformation: Understanding Material Behavior Log in to Mediaspace to watch this video

Explores stress, strain, elasticity, plasticity, and material behavior, emphasizing the importance of dislocations and microstructure.

Deep Generative Models: Part 2 Log in to Mediaspace to watch this video

Explores deep generative models, including mixtures of multinomials, PCA, deep autoencoders, convolutional autoencoders, and GANs.

Page 2 of 2