Introduction: Neurobotics

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Related lectures (32)

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Self-supervised Learning for Autonomous Vehicles

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

Introduction to Image Classification

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

Reinforcement Learning Concepts

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

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

Covers the fundamental concepts of machine learning, including classification, algorithms, optimization, supervised learning, reinforcement learning, and various tasks like image recognition and text generation.

Brain Intelligence: Continual Learning of Representational Models

Delves into the continual learning of representational models in brain intelligence, emphasizing rapid adaptation to unstructured environments.

Improving Models of the Ventral Visual Pathway

Explores computational models of the ventral visual system, focusing on optimizing networks for real-world tasks and comparing to brain data.

Image Representation Insights

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

Non conceptual knowledge systems

Explores the impact of Deep learning on Digital Humanities, focusing on non conceptual knowledge systems and recent advancements in AI.

Neural Network Approximation and Learning

Delves into neural network approximation, supervised learning, challenges in high-dimensional learning, and deep learning experimental revolution.