This lecture overviews Imitation Learning (IL) that has many applications in Game Development, robotics training, Autonomous Driving and Computational Cinematography. It covers the following topics in detail: Elements of IL, Behavioral Cloning, Direct Policy Learning, Inverse Reinforcement Learning, Challenges of IL, IL Project in Unity games, IL in Autonomous Driving, Cinematography Shooting.
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Deep Reinforcement Learning
This lecture overviews Deep Reinforcement Learning that has many applications in, e.g., Game playing agents, Self-driving vehicles, Robotics (Robot cleaners) and Stock exchange agents. It covers the following topics in detail: Finite Markov Decision Processes. Elements of RL (actions, states, Policy, Reward, Value function, Q-function). RL algorithms for finding the optimal policy: Dynamic Programming, Monte… Continue reading Deep Reinforcement Learning
Mathematical brain modeling
This lecture overviews Mathematical Brain Modeling that has many applications in Artificial Neural Networks. It covers the following topics in detail: Brain Cells (Sensory and Motor neurons, Interneurons, glia). Neuron main body, axon, dendrites, chemical/electrical synapses. Neuron physiology, Action Potential. Anatomy of the brain: Cerebrum, Cerebellum, brain stem, left and right brain hemisphere, Corpus Callosum,… Continue reading Mathematical brain modeling
Generative Adversarial Networks
This lecture overviews Generative Adversarial Networks that have many applications in Media Production. It covers the following topics in detail: Theoretical ML background (cross-entropy loss for binary classification), Deep fake, Generator function, Discriminator function, GANs training using Minimax optimization or Heuristic optimization. The most notable GAN architectures are presented: cGAN, IcGAN, Convolutional GANs, LSTM-GAN, TP-GAN,… Continue reading Generative Adversarial Networks
Adversarial Machine Learning
This lecture overviews Adversarial Machine Learning that has many applications in DNN robustness and in privacy protection. It covers the following topics in detail: Adversarial Examples, Attack Methods, Adversarial Face De-Identification, Adversarial Defenses.
Deep Semantic Image Segmentation
Semantic image segmentation is a very important computer vision task with several applications in autonomous systems perception, robotic vision and medical imaging. Recent semantic image segmentation methods rely on deep neural networks and aim to assign a specific class label to each pixel of the input image. This lecture overviews the topic and addresses some… Continue reading Deep Semantic Image Segmentation