Deep Learning and Reinforcement Learning (BAI701)

Deep Learning and Reinforcement Learning

Course Code BAI701 
CIE Marks 50
Teaching Hours/Week (L:T:P: S) 3:0:2:0 
SEE Marks 50
Total Hours of Pedagogy 40 hours Theory + 8-10 Lab slots 
Total Marks 100
Credits 04 

MODULE-1

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

MODULE-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

MODULE-3

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

MODULE-4

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

MODULE-5

Deep Reinforceme,nt 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

PRACTICAL COMPONENT OF IPCC

Experiments

1 Design and implement a neural based network for generating word embedding for words in a

document corpus

2 Write a program to demonstrate the working of a deep neural network for classification task.

3 Desing and implement a Convolutional Neural Network(CNN) for classification of image

dataset

4 Build and demonstrate an autoencoder network using neural layers for data compression on

image dataset.

5 Desing and implement a deep learning network for classification of textual documents.

6 Design and implement a deep learning network for forecasting time series data.

7 Write a program to enable pre-train models to classify a given image dataset

8 Simple Grid World Problem: Design a custom 2D grid world where the agent navigates from

a start position to a goal, avoiding obstacles. Environment: Custom grid (easily implemented

in Python)

Suggested Learning Resources:

Text Book:

1. M. Arif Wani Farooq Ahmad Bhat Saduf Afzal Asif Iqbal Khan, Advances in Deep Learning,

Springer, 2020

2. Ian Goodfellow, Yoshua Bengio, Aaron Courville, “Deep Learning”, MIT Press, 2016.

3. Charu C. Aggarwal, “Neural Networks and Deep Learning”, Springer, 2018.

Reference book:

1. Bengio, Yoshua. "Learning deep architectures for AI." Foundations and trends in Machine

Learning, 2009

2. N.D. Lewis, “Deep Learning Made Easy with R: A Gentle Introduction for Data Science”, January

2016

3. Nikhil Buduma, “Fundamentals of Deep Learning: Designing Next-Generation Machine

Intelligence Algorithms”, O’Reilly publications