CS 337 + 335 : Artificial Intelligence and Machine Learning Labs
The topics covered in each lab are as follows:
- Lab1 : Multi-class perceptron classifier (1v1 and 1vr methods) on data set of shape images
- Lab2 : Feed-forward NNs and CNNs on MNIST and CIFAR-10 datasets
- Lab3 : Ensemble Learning algorithms, Bagging and Boosting (AdaBoost)
- Lab4 : Clustering, k-means and k-medians clustering, their variants and their application on image compression
- Lab5 : Regularization (Ridge, Lasso regression) and Validation (k-fold cross validation)
- Lab6 : MDP (Markov Decision Process) planning using value iteration and its application in maze solvers
- Lab7 : Solving sudoku puzzle using various search algorithms like A\*-search
- Lab8 : Finding approximate solutions of Travelling Salesman Problem (TSP) using various techniques like hill-climbing
Note: CIFAR-10 dataset has been removed from Lab2 due to its 200MB size