Course Work

Sources Referred: Class slides and sources reffered in the slides

Learnings: Techniques for trustworthy aspects of AI: Explainability, Robustness, Reliability, Veification and Validation, Fairness and Bias, Accountability, Ethics in AI

Project and Term Paper presentation

Sources Referred: Class notes, Gilbert Strang, Stephen Boyd

Learnings: Linear Algebra, Vector Calculus, Optimization Algorithms, Convex Optimization and its applications in Machine Learning

Sources Referred: Class notes, PRML by Bishop, MLAPP by Kevin Murphy

Learnings: Linear Regression, MLE, Gaussian MLE, Bayesian Linear Regression (MAP Estimate, Posterior Predictive Distribution),  Polynomial Curve Fitting, Bias-Variance Tradeoff, Conjugate Distributions, Beta Bernoulli Model, Drichllet Distribution, Gaussian-Gamma Distribution, Exponential Family of functions, Discriminative Classification via Logistic Regression, Bayesian Logistic Regression, Laplace Approximations, Generative Models (Naive Bayes), Latent Variables, EM Algorithm, Mixture of Gaussians, Variational Inference, Sampling Methods(Inverse Transform Sampling, Rejection Sampling, Importance Sampling, Monte Carlo Methods, Markov Chains, Metropolis-Hastings, Gibbs Sampling), Gaussian Processes, Bayesian Optimization

Project Presentation

Sources Referred: Class Slides, Andrew Ng Class Notes Stanford, Lectures by Dr. Tanmoy Chakraborty

Learnings: Linear & Logistic Regression (Univariate, Multivariate, Probabilistic Interpretation, Correlated data), Generalized Linear Model, Evaluation and Measuring Generalization (Accuracy, Precision, Confusion Matrix, Cross Validation, Operating Curves: ROC, AUC), Support Vector Machines(Functional & Geometric Margins, Lagrange Multiplier, Primal & Dual, Kernel Trick & Feature Expansion), Decision Trees(Impurity Functions & Uncertainty, Information Gain, Gini Index, Algorithms), Bias-Variance Tradeoff, Overfitting & Underfitting, Ensemble Learning(Bagging & Boosting), KNN, K Means Clustering, Gaussian Mixture Model(Mathematical Model, Covariance Function), EM Algorithm, Naive Bayes Classifier

Project Report

Learnings: How to give good presentations, read research papers, manage citations, writing paper using latex, rules of paper writing

Assignment Papers and Presentation