Continual Learning

This lecture overviews Continual Learning that has many applications in DNN training and adaptation. It covers the following topics in detail: catastrophic forgetting, Regularization CL Methods EWC model), Dynamic CL Approaches (DEN model), Complementary architectures (Fearnet model).

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Explainable AI

This lecture overviews Explainable AI that has many applications in trustworthy AI systems and autonomous systems. It covers the following topics in detail: Interpretability, Interpretability Types (Visual explanations, Image-based Plot visualizations, Textual explanations, Numerical-Mathematical explanations), Explainable AI Applications and Frameworks.

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Imitation Learning

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 Carlo, Temporal-difference learning, SARSA, Q-learning. Deep RL algorithms, DQN and its extensions, Rainbow. Policy Gradient methods. Actor Critic Methods. Imitation Learning. A Maze example is also presented.

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, gyri, sulci, gray matter, white matter. Frontal, Parietal, Temporal, Occipital brain lobes.

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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, Pix2Pix, CycleGAN, StarGAN, GauGAN, DeblurGAN, ID-CGAN, PerceptualGAN, 3D-GAN, MidiNet, StyleGAN, DiscoGAN, PG2.