Lectures related to Geometry and Symmetry in Non-Convex Optimization

Covers the key concepts of PCA, including reducing data dimensionality and extracting features, with practical exercises.

Explores the mechanics of Kirchhoff rods, focusing on 3D kinematics and twisted solutions.

Covers unsupervised learning, focusing on dimensionality reduction and clustering, explaining how it helps find patterns in data without labels.

Discusses regularity conditions, trust regions, and global convergence in optimization.