Advanced Machine Learning: Feature Selection

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RANSAC: Robust Outlier Detection and Applications

Explores the RANSAC algorithm for outlier detection and its diverse applications in Machine Learning.

Financial Time Series Analysis

Covers stylized facts of asset returns, summary statistics, testing for normality, Q-Q plots, and efficient market hypothesis.

Machine Learning Biases

Covers the basics of machine learning, challenges in deployment, adversarial attacks, and privacy concerns.

Machine Learning at the Atomic Scale

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

Introduction to Machine Learning: Course Overview and Basics

Introduces the course structure and fundamental concepts of machine learning, including supervised learning and linear regression.

Image Representation Insights

Explores the evolution of image representation, challenges in supervised learning, benefits of self-supervised learning, and recent advancements in SSL.

Gradient-Based Algorithms in High-Dimensional Learning

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

Self-Supervised Learning: State of the Art

Explores self-supervised learning, transfer learning, SSL prediction tasks, feature learning, image rotations, contrastive learning, and vision learners.

Statistical Learning Theory: Conclusions on Deep Learning

Covers the conclusions on deep learning and an introduction to statistical learning theory.

Nonlinear Supervised Learning

Explores the inductive bias of different nonlinear supervised learning methods and the challenges of hyper-parameter tuning.