Course

Unit 1

Deep Learning: Artificial Neurons – the Building Blocks of Deep Learning, Feed-Forward Deep Neural Networks (DNN), Architectural Considerations in Deep Learning: Activation Functions in Deep Learning, Loss Functions in Deep Learning, Optimizers in Deep Learning: Gradient Descent and Error Back-Propagation, Stochastic Gradient Descent and Adaptive Learning Rate, Hyper-Parameter Selection, Regularization; Convolutional Neural Networks: Convolutional Layer, Pooling Layer, Flattened and Fully Connected Layers; Recurrent Neural Networks, LSTM, Deep learning examples.

Unit 2

Reinforcement Learning: Agents, environments, State and action, Reward, Reinforcement learning as a Markov Decision Process (MDP), Value Functions & Bellman Equations, Prediction and Control by Dynamic Programing, Monte Carlo Methods for Model Free Prediction and Control, Temporal difference learning, Function Approximation Methods, Policy Gradients., Applications in industrial automation and Robotics

Unit 3

Deep Reinforcement Learning Algorithms: Policy-based Algorithms, Value-based Algorithms, Model-based Algorithms, Combined Methods, On-policy and Off-policy Algorithms, Deep Reinforcement Learning for the automation and manufacturing applications.

Course Objectives

1. Implement and use backpropagation algorithms to train deep neural networks
2. Apply regularization techniques to training deep neural networks
3. Apply optimization techniques to training deep neural networks

Course Outcomes

Skills Acquired

Knowledge on deep learning networks, design of automation systems with deep reinforcement learning models for process optimization

1. Ian Goodfellow, Yoshua Bengio and Aeron Courville, Deep Learning, MIT Press, First Edition,
2. Richard Sutton and Andrew G. Barto, Reinforcement Learning: An Introduction, 2nd Edition, The MIT Press, 2018
3. Hao Dong, Zihan Ding, and Shanghang Zhang, Deep Reinforcement Learning: Fundamentals, Research and Applications, Springer, 2020
4. Laura Graesser and Wah Loon Keng, Foundations of Deep Reinforcement Learning: Theory and Practice in Python, AddisonWesley, 2020
5. Sudharsan Ravichandiran, Hands-On Reinforcement Learning with Python: Master reinforcement and deep reinforcement learning using OpenAI Gym and TensorFlow, .2nd Edition, 2020.