Lectures related to Data-Driven Modeling of Haemodynamics in Vascular Flows

AI for Science

Explores fundamental principles in scientific research, the impact of computers, numerical algorithms, and deep learning in solving high-dimensional problems.

Gradient-Based Algorithms in High-Dimensional Learning

Provides insights on gradient-based algorithms, deep learning mysteries, and the challenges of non-convex problems.

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.

Veridical Data Science: Responsible, Reliable, Reproducible

By Prof. Bin Yu explores veridical data science, emphasizing responsible, reliable, and reproducible data analysis and decision-making.

Differentiable Ranking and Sorting

Explores differentiable ranking and sorting techniques for machine learning applications.

Safe Machine Learning: Cryptographic Opportunities

Explores the intersection between machine learning and cryptography, focusing on safe machine learning through cryptographic tools and models.

Understanding Machine Learning: Exactly Solvable Models

Explores the statistical mechanics of learning, focusing on neural networks' mysteries and computational challenges.

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Intelligent Maintenance Systems

Discusses faults in intelligent maintenance, Denoising Sparse Wavelet Network, Koopman operator theory, and Graph Neural Networks.

Neural Networks: Training and Activation

Explores neural networks, activation functions, backpropagation, and PyTorch implementation.

Systems for Health, Security, Energy and the Environment

Covers the Swiss Research Program on Engineering Multi-Scale Systems for Health, Security, Energy, and the Environment.

Physics-inspired Machine Learning for Materials Discovery

Delves into physics-inspired machine learning for materials discovery, emphasizing atomic-scale modeling, thermodynamics, anharmonic free energies, and symmetry in machine learning models.

Data-Driven Modeling in Neuroscience: Meenakshi Khosla

By Meenakshi Khosla explores data-driven modeling in large-scale naturalistic neuroscience, focusing on brain activity representation and computational models.

Deep Learning: Data, Models, and Challenges

Provides an overview of deep learning concepts, focusing on data, model architecture, and challenges in handling large datasets.

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.

Optical Machine Learning: Harnessing Multiple Scattering for Efficiency

Explores the use of optics in machine learning, focusing on large-scale random matrix multiplication through multiple scattering of light.

Deep Learning Fundamentals

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

CIS Open Campus Event 2020: Progress & Collaboration in Intelligent Systems

Covers the CIS Open Campus Event 2020, featuring progress reports, collaboration grants, panel discussions, and a keynote on human-centric mobility.

Machine Learning and Modern AI: SWOT Analysis

Covers a SWOT analysis of Machine Learning and Artificial Intelligence, exploring strengths, weaknesses, opportunities, and threats in the field.