Open-World Learning for Biomedical Data

Related lectures (31)

Page 3 of 4

Explores the histology of the stomach, emphasizing its body and pyloric portions.

Covers the fundamental principles and methods of machine learning, including supervised and unsupervised learning techniques.

Explores deep and convolutional networks, covering generalization, optimization, and practical applications in machine learning.

Explores learning from interconnected data using graphs, covering challenges, GNN design, research landscapes, and democratization of Graph ML.

Explores cell formation, anatomy, water flow, and growth rings in hardwoods and softwoods.

Covers supervised learning with a focus on linear regression, including topics like digit classification, spam detection, and wind speed prediction.

Explores characterizing cell types based on various modalities and the evolving field of single-cell RNA sequencing in neuroscience.

Covers molecular neuroscience, neurodegeneration, cell types, neuron structure, and history of neuroscience breakthroughs.

Explores simple models, electronic structure evaluation, and machine learning at the atomic scale.

Explores image classification using decision trees and random forests to reduce variance and improve model robustness.