Machine Learning Theory: High-Dimensional Perspectives

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Gives an overview of EPFL's School of Computer and Communication Sciences, highlighting its top-ranked faculty and research areas.

Introduction to Machine Learning: Supervised Learning

Introduces supervised learning, covering classification, regression, model optimization, overfitting, and kernel methods.

Dimensionality Reduction

Introduces artificial neural networks and explores various dimensionality reduction techniques like PCA, LDA, Kernel PCA, and t-SNE.

Extended Josephson Junctions: Modeling and Critical Current

Covers the magnetic thickness, short and long junctions, and critical current.

Unsupervised Learning: Dimensionality Reduction

Explores unsupervised learning techniques for reducing dimensions in data, emphasizing PCA, LDA, and Kernel PCA.

Flexibility of Models & Bias-Variance Trade-Off

Delves into the trade-off between model flexibility and bias-variance in error decomposition, polynomial regression, KNN, and the curse of dimensionality.

Neural Networks

Explores neural networks, hidden layers, weight adjustments, activation functions, and the universal approximation theorem.

Introduction to Data Science

Introduces the basics of data science, covering decision trees, machine learning advancements, and deep reinforcement learning.

Statistical Physics of Learning

Offers insights into the statistical physics of learning, exploring the relationship between neural network structure and disordered systems.

Curse of Dimensionality in Deep Learning

Delves into the challenges of deep learning, exploring dimensionality, performance, and overfitting phenomena in neural networks.