Course Syllabus:
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)
Coding tutorials & hands-on: [in parallel, led by the TAs]
- Intro to Google CoLab, Pytorch
- CNN, LSTM, AE and GAN coding
Online Lecture Management:
- Google Classroom [invitation based, exclusive to those who officially register]