Teaching

Autumn 2025, Autumn 2024

AI61201: Visual Computing with AI/ML

Course Instructors: Somdyuti Paul
Syllabus

  • Image formation and representation
  • The human visual System and modeling visual perception
  • Image transformations in the spatial domain
  • Image analysis and filtering in the frequency domain
  • Multi-resolution representations for images
  • Feature design for image analysis and recognition
  • Deep neural networks for vision tasks
  • Generative models for visual data
  • Representation and analysis of videos
  • Perceptual quality and compression models for visual data

Spring 2025

AI61002: Deep Learning Foundations and Applications

Syllabus:

  • Introduction and historical trends in deep learning
  • Perceptron and deep feedforward neural networks
  • Backpropagation and optimization using gradient descent based methods
  • Regularization techniques for deep networks
  • Introduction to convolutional neural networks, standard CNN architectures and their use for classification and regression
  • Types of convolution operations and backpropagation through convolutional layers
  • Visualization of CNN filters and gradients, and adversarial attacks
  • Sequence modeling using recurrent neural networks, types of recurrent units and their roles in resolving long-term dependency issues
  • Introduction to attention mechanisms, transformers, and language modeling using BERT and GPT
  • Autoencoders and their applications
  • Adversarial learning and generative adversarial networks

Spring 2024 (Co-taught part of the course)

AI42001: Machine Learning Foundations and Applications

Syllabus

  • Introduction to machine learning, basics of supervised, unsupervised and reinforcement learning
  • k nearest neighbours
  • Linear models for classification and regression
  • Bayesian learning and naive Bayes model
  • Bias-variance tradeoff
  • Decision Trees
  • MLE and MAP estimation
  • Support vector machines
  • Feedforward neural networks
  • Convolutional and Recurrent Neural networks
  • Ensemble methods
  • Clustering
  • Dimensionality reduction using PCA and LDA
  • Hidden Markov Models
  • Overview of large language models