Relation of SARSA and Bellman equation

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Planning the First Draft

Covers the basic requirements for the first draft and guides students on project planning and outlining techniques.

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.

Hidden Exposure: Measuring US Supply Chain Reliance

Explores the hidden exposure of US supply chains to foreign suppliers and the importance of simplifying complex realities through mental models.

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.

TD Learning: Temporal Difference Learning

Covers Temporal Difference Learning, V-values, state-values, and TD methods in reinforcement learning.

Convolutional Neural Networks

Introduces Convolutional Neural Networks (CNNs) for autonomous vehicles, covering architecture, applications, and regularization techniques.

Reinforcement Learning: One-step Horizon (Bandit Problems)

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

Unintended Consequences of Technology in Global South

Examines unintended negative impacts of technology in sustainable development, addressing challenges and consequences in the Global South.

Ethics and Law of AI

Delves into different ethical theories and thought experiments in the context of AI, exploring the challenges of defining AI.

Generative Models: Trajectory Forecasting

Explores generative models for trajectory forecasting in autonomous vehicles, including discriminative vs generative models, VAES, GANS, and case studies.