Reinforcement Learning: Q-Learning

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Model-Based Deep Reinforcement Learning: Monte Carlo Tree Search

Explores model-based deep reinforcement learning, focusing on Monte Carlo Tree Search and its applications in game strategies and decision-making processes.

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.

Deep Learning: Fundamentals

Covers the fundamentals of deep learning, from artificial neurons to modern networks.

Neuroscience and AI: Bridging the Gap

Explores the gap between AI and human intelligence through neuroscience-inspired models and algorithms.

Non-Conceptual Knowledge Systems

Delves into the impact of deep learning on non-conceptual knowledge systems and the advancements in transformers and generative adversarial networks.

Introduction to Reinforcement Learning: Concepts and Applications

Introduces reinforcement learning, covering its concepts, applications, and key algorithms.

Reinforcement Learning Fundamentals

Delves into the fundamentals of reinforcement learning, discussing states, actions, rewards, policies, and neural network applications.

Deep Learning: Data Representations and Neural Networks

Covers data representations, Bag of Words, histograms, data pre-processing, and neural networks.

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.

Reinforcement Learning: Reward-based Learning

Explores artificial neural networks, reward information in the brain, animal conditioning, deep reinforcement learning, and a quiz on rewards.