Implemented following models-

• Binary Bayes Classifiers from data with Max. Likelihood.
• Multiclass Bayes Classifiers from data with Max. Likelihood.
• Bias-Variance analysis in regression.
• Analyse overfitting and underfitting in Regression.

Implemented following models-

• Implemented Logistic Regression with RBF kernel and did the hyperparamter tunning(kernel parameter, regulariser, learning rate).
• Implemented SVM for different kernel type(RBF, linear, poly).
• Implemented Decision tree with purning(at what height stop spliting).
• Implemented Random Forest Classifier and tuned with(hyper parameter- Fraction of data to learn tree=0.5, Fraction of number of features taken per data=0.5).

Implemented following models-

• Run k-Nearest neighbours on the binary classification dataset for classifiying whether a given movie is a comedy or not.
• Implemented PCA and regression.
• Implemented Baseline methods for collaborative filtering.
• Implemented EM algorithm for Gaussian Mixture models.

Implemented following model-

• Implemented Guassian Mixture Model.
• Implemented PCA, KPCA.

Implemented different models for classification of ham-spam mails.

In this contest we had to predict the movie rating, the famous netflix challenge.

we tried different collaborative filtering method. Ex-Nearest neighbourhood models, Modified latent factor model.

Our best result came with Modified latent factor model.

Contest was hosted on kaggle https://www.kaggle.com/c/prml19/leaderboard .