Local Learning Rules: Representations and Actions

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Explores the learning dynamics of deep neural networks using linear networks for analysis, covering two-layer and multi-layer networks, self-supervised learning, and benefits of decoupled initialization.

Landscape and Generalisation in Deep Learning

Explores the challenges and insights of deep learning, focusing on loss landscape, generalization, and feature learning.

Image Representation Insights

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

Machine Learning Fundamentals

Introduces fundamental machine learning concepts, covering regression, classification, dimensionality reduction, and deep generative models.

Models of Long-Term Plasticity: Hebbian Learning

Explores Hebbian learning, synaptic plasticity, and models for long-term plasticity.

Active Learning for Molecular Design

Covers machine learning approaches for material design, practical examples, and software tools for research.

Different types of learning

Covers supervised, unsupervised, and reinforcement learning in neurorobotics.

Deep Learning Modus Operandi

Explores the benefits of deeper networks in deep learning and the importance of over-parameterization and generalization.

Reinforcement Learning Concepts

Covers key concepts in reinforcement learning, neural networks, clustering, and unsupervised learning, emphasizing their applications and challenges.

Learning and synaptic plasticity

Covers sensory stimulation, learning, synaptic plasticity, and Hebb's postulate in neurorobotics.