Introduction to Deep Learning: Introduction, Shallow Learning, Deep Learning, Why to use Deep Learning, How Deep Learning Works, Deep Learning Challenges, How Learning Differs from Pure Optimization, Challenges in Neural Network Optimization.

Textbook 1: Ch 1.1 – 1.6, Textbook 2: 8.1,8.2

Basics of Supervised Deep Learning: Introduction, Convolution Neural Network, Evolution of Convolution Neural Network, Architecture of CNN, Convolution Operation

Textbook 1: Ch 2.1 – 2.5

Training Supervised Deep Learning Networks: Training Convolution Neural Networks, Gradient Descent-Based Optimization Techniques, Challenges in Training Deep Networks.

Supervised Deep Learning Architectures: LetNet-5, AlexNet

Text Book - 1 : Ch 3.2,3.4,3.5, Ch 4.2,4.3

Recurrent and Recursive Neural Networks: Unfolding Computational Graphs, Recurrent Neural Network, Bidirectional RNNs, Deep Recurrent Networks, Recursive Neural Networks, The Long Short-Term Memory. Gated RNNs.

Text Book – 2: 10.1-10.3, 10.5, 10.6, 10.10

Deep Reinforcement Learning: Introduction, Stateless Algorithms: Multi-Armed Bandits, The Basic Framework of Reinforcement Learning, case studies.

Textbook – 3: Chapter 9: 9.1,9.2,9.3, 9.7