Lectures related to Reinforcement Learning: Q-Learning

Autonomous Vehicles: Intelligence and Perception

Explores intelligence, perception, and AI applications in autonomous vehicles, emphasizing rational thinking and social intelligence.

Collective Learning Dynamics: Similarity Exploitation

Delves into collective learning dynamics with similarity exploitation, covering structured learning, adaptive frameworks, modeling, simulation, and experimental results.

Reinforcement Learning: Policy Gradient and Actor-Critic Methods

Provides an overview of reinforcement learning, focusing on policy gradient and actor-critic methods for deep artificial neural networks.

Policy Gradient and Actor-Critic Methods: Eligibility Traces Explained

Discusses policy gradient and actor-critic methods, focusing on eligibility traces and their application in reinforcement learning tasks.

Self-supervised Learning for Autonomous Vehicles

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

Reinforcement Learning: One-step Horizon (Bandit Problems)

Covers Bandit Problems in Reinforcement Learning, focusing on one-step horizon games and Q-values.

Autonomous Vehicles: Trajectory Prediction and Social Behavior

Explores challenges in deep learning for autonomous vehicles, emphasizing social behavior modeling and feasible trajectory prediction.

Landscape and Generalisation in Deep Learning

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

Reinforcement Learning: Q-Learning

Covers Q-Learning in reinforcement learning, exploring action values, policies, and the societal impact of algorithms.

Neural Networks: Multilayer Learning

Covers the fundamentals of multilayer neural networks and deep learning, including back-propagation and network architectures like LeNet, AlexNet, and VGG-16.

Socially-aware Artificial Intelligence for Last-mile Mobility

Explores socially-aware AI for last-mile mobility, focusing on understanding social etiquettes, anticipating behaviors, and forecasting crowd movements.

Trajectory Forecasting in Autonomous Vehicles

Explores trajectory forecasting in autonomous vehicles, focusing on deep learning models for predicting human trajectories in socially-aware transportation scenarios.

Deep Learning for Autonomous Vehicles: Learning

Explores learning in deep learning for autonomous vehicles, covering predictive models, RNN, ImageNet, and transfer learning.

Deep Learning for Autonomous Vehicles: Predictive Models

Explores predictive models and trackers for autonomous vehicles, covering object detection, tracking challenges, neural network-based tracking, and 3D pedestrian localization.

General Introduction into Artificial Neural Networks: part 3

Covers learning by rewards in deep reinforcement learning without math details.

Reinforcement Learning: TD Learning and SARSA Variants

Discusses reinforcement learning, focusing on temporal difference learning and SARSA algorithm variations.

Neural Networks Optimization

Explores neural networks optimization, including backpropagation, batch normalization, weight initialization, and hyperparameter search strategies.

Multilayer Neural Networks: Deep Learning

Covers the fundamentals of multilayer neural networks and deep learning.

Theory of reinforcement learning: Grid examples

Explains the theory of reinforcement learning through grid examples and iterative Q-value updates.

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.