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7th-Sem

Semester-VII (Practice and Homework Assignments)

AI1001: Introduction to Modern AI

  • Classical AI
  • SVM
  • Neural Networks
  • Logistic Regression
  • Reinforcement Learning

CH2030 (Numerical Methods I)

  • Non-iterative Linear: Gauss Elimination, Gaus Jordan, LU decomposition
  • Iterative Linear: Gauss Jacobi, Gauss Seidel, Succesive Over relaxation
  • Iterative Non-Linear: Successive Subsitution, Newton Raphson

CS6890 (Fraud Analytics)

  • Identifying fraudulent Taxpayers using Spectral Clustering
  • Identifying fraudulent taxpayers using variational autoencoders
  • Example Dependent cost sensitive logistic regression
  • Example dependent cost sensitive classification using deep neural net
  • Collusion Set Detection using Graph Clustering

EE5377 (Image Processing)

  • Binary Morphology
  • Zooming: Nearest neighbor, Bilinear interpolation.
  • 2D-DFT
  • Convolution
  • Linear Filters

EE5601 (Representation Learning)

  • Kmeans Algorithm
  • Principle Component Analysis
  • Maximum Likelihood estimation
  • EM algorithm for gaussian mixtures
  • MLP from scratch
  • Autoencoder and Sparse autoencoder on MNIST

EE5602 (Probabilistic Graphical Models)

  • Disparity estimation using sum-product algorithm
  • Speech classification using HMM