Introducing a formal framework for representing learning

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Local Learning Rules: Representations and Actions

Explores local learning rules for representations and actions, covering synaptic plasticity, reinforcement learning, and good representations.

Introduction to Machine Learning

Provides an overview of Machine Learning, including historical context, key tasks, and real-world applications.

Machine Learning: Supervised and Unsupervised Learning Techniques

Covers supervised and unsupervised learning techniques in machine learning, highlighting their applications in finance and environmental analysis.

Self-supervised Learning for Autonomous Vehicles

Explores self-supervised learning for autonomous vehicles, deriving labels from data itself and discussing its applications and challenges.

Perception: Data-Driven Approaches

Explores perception in deep learning for autonomous vehicles, covering image classification, optimization methods, and the role of representation in machine learning.

Introduction to Image Classification

Covers image classification, clustering, and machine learning techniques like dimensionality reduction and reinforcement learning.

Classification of Plasticity: Short-term vs. Long-term Changes

Explores short-term and long-term synaptic changes, Hebbian learning, and the role of neuromodulators.

Landscape and Generalisation in Deep Learning

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

Machine Learning Fundamentals

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

Active Learning for Molecular Design

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