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