EC61409: Neural Networks And Applications

EC61409
Course name	Neural Networks And Applications
Offered by	Electronics & Electrical Communication Engineering
Credits	3
L-T-P	3-0-0
Professor(s)	http://www.ecdept.iitkgp.ac.in/Eece/facultydetails/ece-dsen
Previous Year Grade Distribution
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Semester	Autumn

Syllabus

Concepts taught in class

Artificial Neuron, Neuronal Network and Regression:

- McCulloch–Pitts Neuron model

- Network Architecture, Design and Learning

- Linear regression

Unconstrained Optimization and Least Mean Square (LMS):

- Unconstrained Optimization

- LMS algorithm and its structure

Perceptron:

- Rosenblatt’s Perceptron

- Perceptron Convergence Algorithm

- Bayes Classification & Logistic Regression

- Batch Perceptron Algorithm

Multilayer Perceptron and Back Propagation:

- Basic Architecture, Batch and Online Learning

- Back Propagation Algorithm and its Attributes

- Back Propagation Heuristics & More

Convolutional Neural Networks (CNN):

- CNN computations

- Training the CNN (hyperparameter & optimization choices)

- CNN architectures

Generative Adversarial Networks (GAN):

- Construction of GAN, Deep Convolutional GAN

- GAN variants: Conditional GAN, Wasserstein GAN, Cycle GAN, PatchGAN, InfoGAN, BiGAN, RealnessGAN

Recurrent Neural Networks (RNN), Transformers, Auto Encoders & GNN:

- RNN, Bidirectional RNN, Long Short Term Memory (LSTM) Network

- Transformer & Attention networks

- Classical, Adversarial and Variational Deep Auto Encoders

- Contrastive & Competitive Learning

- Introduction to Graph Neural Network (GNN)

Time Table

Day	8:00-8:55 am	9:00-9:55 am	10:00-10:55 am	11:00-11:55 am	12:00-12:55 pm		2:00-2:55 pm	3:00-3:55 pm	4:00-4:55 pm	5:00-5:55 pm
Monday	F300	F300
Tuesday					F300
Wednesday
Thursday
Friday