Lectures related to Great-circle distance

Geodesics on Surfaces

Explores geodesics on surfaces, focusing on minimizing distances and properties of paths, with examples like great circles on spheres.

Projective Geometry: Historical Overview

Explores the historical development of projective geometry and the contributions of key figures like Alberti and Piero della Francesca.

Orthonormal Basis in 3D Space

Introduces orthonormal bases in 3D space and their applications in geometry.

Thematic attributes and classification

Covers the creation of thematic maps and the class setting in statistical thematic mapping.

Triangles Isométriques

Discusses isometric triangles, their properties, conditions for determination, and distance between points.

Analytical Study of Plan: Points, Lines

Covers the analytical study of the plan, focusing on points and lines.

Riemannian distance, geodesically convex sets

Covers the structure of Riemannian manifolds, geodesic convexity, and the Riemannian distance function.

Data Summarization: Minhashing and Locality-Sensitive Hashing

Explores Jaccard similarity, minhashing, and locality-sensitive hashing for data summarization.

Orthogonality and Least Squares Method

Covers orthogonal vectors, unit vectors, and the Pythagorean theorem in R^m.

Projective Spaces: Separation and Definitions

Covers separated spaces, saturation properties, and projective spaces, including the real projective plane and compactness.

Embedding Space: AdS and Euclidean Geometry

Covers the concept of embedding space for AdS and Euclidean geometry, discussing AdS isometries and Euclidean Ads.

Geodesic Curves: Minimizing Distance and Constant Speed

Covers geodesic curves parameterized at constant speed to minimize distances between points.

Analysis of ARV

Covers the analysis of ARV, focusing on minimizing distances between points in cutis vis.

Linear Classification: Signed Distance and Perceptron

Explores signed distance, perceptron, logistic regression, cross entropy, and multi-class classification.

Introduction to Module 2

Explores the shift to online education, benefits, challenges, and needs analysis.

The Conjugate Gradients Method (CG)

Covers the Conjugate Gradients method for solving linear systems iteratively with quadratic convergence and emphasizes the importance of linear independence among conjugate directions.

Physically-based Differentiable Rendering

Covers Physically-based Differentiable Rendering in the context of Realistic Graphics Lab at EPFL, exploring its applications and future potential.