This repo contains the assignments from Machine Learning class from Professor Cetina (summer term 2019).

Machine Learning

Assignment 1:

Modeling noisy sine wave using third degree polynomial linear regression and training the model using stochatic gradient descend.

Assignment 2:

Modeling the linear classifier using logistic regression and training using stochatic gradient descend.

Assignment 3:

Using Gaussian Discrimination Analysis(GDA) to model the classifier for chagas paracite diagnosis. The training data is 30 positive and 30 negative examples.

Assignment 4:

Classifying the same chagas paracite samples used in exercise 3, using Support Vector Machines(SVM).

Assignment 5:

Implementation of Adaboost for classifying very noisy data by combining several weak classifiers

Assignment 6:

Implemenation of Expectation-Maximization(EM) for Gaussian Mixture Model(GMM) for clustering the data coming from 3 gaussian models.(Partially Done)

Assignment 7:

Implemention of 10-arms bandit problem using epsilon-greedy policy and comparison of various epsilon values. Also, comparison of e-greedy with or w/o Upper Confidance Boundary(UCB). An optimisitic uniform initialization is also explored.

Assignment 8:

Iterative policy evaluation using Dynamic programing(DP) for random policy. Solved the grid world problem mentioned in sutton and Batro at page 76.

Language Technology

Assignment 3:

Finding most frenquent unigram, bigram, trigram in the given training corpora.

Assignment 10:

Implementaion of pageRank algorithm using matrix formula and iterative method both.