Lectures related to OpenAI | EPFL Graph Search

Custom GPTs for Academic Writing

Delves into the use of custom GPTs for academic writing, highlighting the balance between AI assistance and traditional learning methods.

Differentiation: Continuity vs Differentiability

Explores the relationship between continuity and differentiability of functions, highlighting examples where functions exhibit different properties at specific points.

Limits and Continuity: Analysis 1

Explores limits, continuity, and uniform continuity in functions, including properties at specific points and closed intervals.

Limits: Basic Properties and Examples

Covers the basic properties of limits and provides illustrative examples.

Taylor Approximation: Understanding the Basics

Explores Taylor approximation for function approximation and error control.

Martingales and Conditional Expectations

Explores Brun's Sieve, Martingales, and Conditional Expectations in probability theory.

Limits at Infinity: Algebra and Continuity

Covers limits at infinity, algebra, and continuity with examples.

Subsequences and Bolzano-Weierstrass Theorem

Covers the proof of the Squeeze Theorem, Quotient Criteria, and the Bolzano-Weierstrass Theorem.

Matrix Similarity and Diagonalization

Explores matrix similarity, diagonalization, characteristic polynomials, eigenvalues, and eigenvectors in linear algebra.

Limits of Sequences

Covers the concept of limits of sequences, including definitions, examples, boundedness, and more.

Canonical Embedding: Fields and Lattices

Explores canonical embedding of #-fields, lattices, and finiteness of class groups.

Polynomial Interpolation: Lagrange Method

Covers the Lagrange polynomial interpolation method and error analysis in function approximation.

Rolle's Theorem and Mean Value Theorem

Covers Rolle's Theorem and the Mean Value Theorem, discussing stationary points and extrema.

Thomas algorithm, accuracy of direct methods

Explores the Thomas algorithm for tridiagonal systems and the accuracy of direct methods in numerical computations.

Value Tensions in AI: Mitigating Harms and Fostering Benefits

Delves into value tensions in AI ethics, balancing inclusion and privacy.

NFNets: Removing BatchNorm for High-Performance Image Recognition

Explores NFNets as an alternative to BatchNorm in ResNets, achieving high performance on ImageNet.

Real Numbers Sequences

Covers real number sequences, including convergence and boundedness concepts.

Generative AI and Reinforcement Learning: Future Directions

Explores advancements in generative AI and reinforcement learning, focusing on their applications, safety, and future research directions.

Diagonalization of Symmetric Matrices

Covers the diagonalization of symmetric matrices, the spectral theorem, and the use of spectral decomposition.

Deep Learning for Autonomous Vehicles: Learning

Explores learning in deep learning for autonomous vehicles, covering predictive models, RNN, ImageNet, and transfer learning.